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Found 165 papers, 83 papers with code
Defense Against Adversarial Attacks via Controlling Gradient Leaking on Embedded Manifolds
Based on our investigation, we further present a new robust learning algorithm which encourages a larger gradient component in the tangent space of data manifold, suppressing the gradient leaking phenomenon consequently.
Beta Distribution Guided Aspect-aware Graph for Aspect Category Sentiment Analysis with Affective Knowledge
To be specific, we first regard each aspect as a pivot to derive aspect-aware words that are highly related to the aspect from external affective commonsense knowledge.
Omniview-Tuning: Boosting Viewpoint Invariance of Vision-Language Pre-training Models
Vision-Language Pre-training (VLP) models like CLIP have achieved remarkable success in computer vision and particularly demonstrated superior robustness to distribution shifts of 2D images.
Exploring the Transferability of Visual Prompting for Multimodal Large Language Models
To achieve this, we propose Transferable Visual Prompting (TVP), a simple and effective approach to generate visual prompts that can transfer to different models and improve their performance on downstream tasks after trained on only one model.
FaceCat: Enhancing Face Recognition Security with a Unified Generative Model Framework
Motivated by the rich structural and detailed features of face generative models, we propose FaceCat which utilizes the face generative model as a pre-trained model to improve the performance of FAS and FAD.
An N-Point Linear Solver for Line and Motion Estimation with Event Cameras
To recover the full linear camera velocity we fuse observations from multiple lines with a novel velocity averaging scheme that relies on a geometrically-motivated residual, and thus solves the problem more efficiently than previous schemes which minimize an algebraic residual.
Embodied Active Defense: Leveraging Recurrent Feedback to Counter Adversarial Patches
The vulnerability of deep neural networks to adversarial patches has motivated numerous defense strategies for boosting model robustness.
CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction Model
In this work, we present the Convolutional Reconstruction Model (CRM), a high-fidelity feed-forward single image-to-3D generative model.
Can Your Model Tell a Negation from an Implicature? Unravelling Challenges With Intent Encoders
The triplet task gauges the model's understanding of two semantic concepts paramount in real-world conversational systems-- negation and implicature.
Semi-Supervised Dialogue Abstractive Summarization via High-Quality Pseudolabel Selection
Semi-supervised dialogue summarization (SSDS) leverages model-generated summaries to reduce reliance on human-labeled data and improve the performance of summarization models.
Abstractive Text Summarization
Natural Language Understanding
DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training
Pre-training has been investigated to improve the efficiency and performance of training neural operators in data-scarce settings.
MAGID: An Automated Pipeline for Generating Synthetic Multi-modal Datasets
Development of multimodal interactive systems is hindered by the lack of rich, multimodal (text, images) conversational data, which is needed in large quantities for LLMs.
BSPA: Exploring Black-box Stealthy Prompt Attacks against Image Generators
It allows users to design specific prompts to generate realistic images through some black-box APIs.
TofuEval: Evaluating Hallucinations of LLMs on Topic-Focused Dialogue Summarization
We find that there are diverse errors and error distributions in model-generated summaries and that non-LLM based metrics can capture all error types better than LLM-based evaluators.
Noise Contrastive Alignment of Language Models with Explicit Rewards
Existing alignment methods, such as Direct Preference Optimization (DPO), are mainly tailored for pairwise preference data where rewards are implicitly defined rather than explicitly given.
Your Diffusion Model is Secretly a Certifiably Robust Classifier
Diffusion models are recently employed as generative classifiers for robust classification.
Preconditioning for Physics-Informed Neural Networks
Physics-informed neural networks (PINNs) have shown promise in solving various partial differential equations (PDEs).
FoVA-Depth: Field-of-View Agnostic Depth Estimation for Cross-Dataset Generalization
Unfortunately, most of the GT data is for pinhole cameras, making it impossible to properly train depth estimation models for large-FoV cameras.
Towards Transferable Targeted 3D Adversarial Attack in the Physical World
However, the field of transferable targeted 3D adversarial attacks remains vacant.
Machine Vision Therapy: Multimodal Large Language Models Can Enhance Visual Robustness via Denoising In-Context Learning
Concretely, by estimating a transition matrix that captures the probability of one class being confused with another, an instruction containing a correct exemplar and an erroneous one from the most probable noisy class can be constructed.
Evil Geniuses: Delving into the Safety of LLM-based Agents
Rapid advancements in large language models (LLMs) have revitalized in LLM-based agents, exhibiting impressive human-like behaviors and cooperative capabilities in various scenarios.
LLaVA-Plus: Learning to Use Tools for Creating Multimodal Agents
LLaVA-Plus is a general-purpose multimodal assistant that expands the capabilities of large multimodal models.
Ranked #1 on
LMM real-life tasks
on Leaderboard
Multi-view learning for automatic classification of multi-wavelength auroral images
Finally, to highlight the discriminative information between auroral classes, we propose a lightweight attention feature enhancement module called LAFE.
Mobile AR Depth Estimation: Challenges & Prospects -- Extended Version
In this paper, we investigate the challenges and opportunities of achieving accurate metric depth estimation in mobile AR.
Towards a General Framework for Continual Learning with Pre-training
In this work, we present a general framework for continual learning of sequentially arrived tasks with the use of pre-training, which has emerged as a promising direction for artificial intelligence systems to accommodate real-world dynamics.
Enhancing Abstractiveness of Summarization Models through Calibrated Distillation
Our experiments show that DisCal outperforms prior methods in abstractive summarization distillation, producing highly abstractive and informative summaries.
Improved Operator Learning by Orthogonal Attention
Neural operators, as an efficient surrogate model for learning the solutions of PDEs, have received extensive attention in the field of scientific machine learning.
Score Regularized Policy Optimization through Diffusion Behavior
Recent developments in offline reinforcement learning have uncovered the immense potential of diffusion modeling, which excels at representing heterogeneous behavior policies.
Hierarchical Decomposition of Prompt-Based Continual Learning: Rethinking Obscured Sub-optimality
Following these empirical and theoretical insights, we propose Hierarchical Decomposition (HiDe-)Prompt, an innovative approach that explicitly optimizes the hierarchical components with an ensemble of task-specific prompts and statistics of both uninstructed and instructed representations, further with the coordination of a contrastive regularization strategy.
A 5-Point Minimal Solver for Event Camera Relative Motion Estimation
Event-based cameras are ideal for line-based motion estimation, since they predominantly respond to edges in the scene.
How Robust is Google's Bard to Adversarial Image Attacks?
By attacking white-box surrogate vision encoders or MLLMs, the generated adversarial examples can mislead Bard to output wrong image descriptions with a 22% success rate based solely on the transferability.
Incorporating Neuro-Inspired Adaptability for Continual Learning in Artificial Intelligence
Continual learning aims to empower artificial intelligence (AI) with strong adaptability to the real world.
A Unified Interactive Model Evaluation for Classification, Object Detection, and Instance Segmentation in Computer Vision
Existing model evaluation tools mainly focus on evaluating classification models, leaving a gap in evaluating more complex models, such as object detection.
AdvFAS: A robust face anti-spoofing framework against adversarial examples
Ensuring the reliability of face recognition systems against presentation attacks necessitates the deployment of face anti-spoofing techniques.
COCO-O: A Benchmark for Object Detectors under Natural Distribution Shifts
To give a more comprehensive robustness assessment, we introduce COCO-O(ut-of-distribution), a test dataset based on COCO with 6 types of natural distribution shifts.
Improving Viewpoint Robustness for Visual Recognition via Adversarial Training
Experimental results show that VIAT significantly improves the viewpoint robustness of various image classifiers based on the diversity of adversarial viewpoints generated by GMVFool.
Towards Viewpoint-Invariant Visual Recognition via Adversarial Training
Visual recognition models are not invariant to viewpoint changes in the 3D world, as different viewing directions can dramatically affect the predictions given the same object.
Distributional Modeling for Location-Aware Adversarial Patches
In this paper, we propose the Distribution-Optimized Adversarial Patch (DOPatch), a novel method that optimizes a multimodal distribution of adversarial locations instead of individual ones.
PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs
In addition to providing a standardized means of assessing performance, PINNacle also offers an in-depth analysis to guide future research, particularly in areas such as domain decomposition methods and loss reweighting for handling multi-scale problems and complex geometry.
DIFFender: Diffusion-Based Adversarial Defense against Patch Attacks
In this paper, we propose DIFFender, a novel defense method that leverages a text-guided diffusion model to defend against adversarial patches.
MultiAdam: Parameter-wise Scale-invariant Optimizer for Multiscale Training of Physics-informed Neural Networks
Physics-informed Neural Networks (PINNs) have recently achieved remarkable progress in solving Partial Differential Equations (PDEs) in various fields by minimizing a weighted sum of PDE loss and boundary loss.
NUNO: A General Framework for Learning Parametric PDEs with Non-Uniform Data
The neural operator has emerged as a powerful tool in learning mappings between function spaces in PDEs.
ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation
In comparison, VSD works well with various CFG weights as ancestral sampling from diffusion models and simultaneously improves the diversity and sample quality with a common CFG weight (i. e., $7. 5$).
Robust Classification via a Single Diffusion Model
Since our method does not require training on particular adversarial attacks, we demonstrate that it is more generalizable to defend against multiple unseen threats.
Ranked #2 on
Adversarial Defense
on CIFAR-10
DIVA: A Dirichlet Process Mixtures Based Incremental Deep Clustering Algorithm via Variational Auto-Encoder
Generative model-based deep clustering frameworks excel in classifying complex data, but are limited in handling dynamic and complex features because they require prior knowledge of the number of clusters.
Assessing the potential of AI-assisted pragmatic annotation: The case of apologies
We find that the Bing chatbot outperformed ChatGPT, with accuracy approaching that of a human coder.
Decision-based iterative fragile watermarking for model integrity verification
Our method has some advantages: (1) the iterative update of samples is done in a decision-based black-box manner, relying solely on the predicted probability distribution of the target model, which reduces the risk of exposure to adversarial attacks, (2) the small-amplitude multiple iterations approach allows the fragile samples to perform well visually, with a PSNR of 55 dB in TinyImageNet compared to the original samples, (3) even with changes in the overall parameters of the model of magnitude 1e-4, the fragile samples can detect such changes, and (4) the method is independent of the specific model structure and dataset.
Adversarial Examples Detection with Enhanced Image Difference Features based on Local Histogram Equalization
However, numerous studies have proven that previous methods create detection or defense against certain attacks, which renders the method ineffective in the face of the latest unknown attack methods.
Text-to-Image Diffusion Models can be Easily Backdoored through Multimodal Data Poisoning
To gain a better understanding of the training process and potential risks of text-to-image synthesis, we perform a systematic investigation of backdoor attack on text-to-image diffusion models and propose BadT2I, a general multimodal backdoor attack framework that tampers with image synthesis in diverse semantic levels.
Meta-Reinforcement Learning Based on Self-Supervised Task Representation Learning
On MuJoCo and Meta-World benchmarks, MoSS outperforms prior works in terms of asymptotic performance, sample efficiency (3-50x faster), adaptation efficiency, and generalization robustness on broad and diverse task distributions.
Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement Learning
The main challenge for this setting is that the intermediate guidance during the diffusion sampling procedure, which is jointly defined by the sampling distribution and the energy function, is unknown and is hard to estimate.
Detection Transformer with Stable Matching
We point out that the unstable matching in DETR is caused by a multi-optimization path problem, which is highlighted by the one-to-one matching design in DETR.
A Closer Look at Parameter-Efficient Tuning in Diffusion Models
Large-scale diffusion models like Stable Diffusion are powerful and find various real-world applications while customizing such models by fine-tuning is both memory and time inefficient.
Towards Effective Adversarial Textured 3D Meshes on Physical Face Recognition
The goal of this work is to develop a more reliable technique that can carry out an end-to-end evaluation of adversarial robustness for commercial systems.
Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous Driving
3D object detection is an important task in autonomous driving to perceive the surroundings.
Rethinking Model Ensemble in Transfer-based Adversarial Attacks
It is widely recognized that deep learning models lack robustness to adversarial examples.
One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale
Inspired by the unified view, UniDiffuser learns all distributions simultaneously with a minimal modification to the original diffusion model -- perturbs data in all modalities instead of a single modality, inputs individual timesteps in different modalities, and predicts the noise of all modalities instead of a single modality.
Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection
To effectively fuse language and vision modalities, we conceptually divide a closed-set detector into three phases and propose a tight fusion solution, which includes a feature enhancer, a language-guided query selection, and a cross-modality decoder for cross-modality fusion.
Ranked #1 on
Zero-Shot Object Detection
on MSCOCO
Task Aware Dreamer for Task Generalization in Reinforcement Learning
Extensive experiments in both image-based and state-based tasks show that TAD can significantly improve the performance of handling different tasks simultaneously, especially for those with high TDR, and display a strong generalization ability to unseen tasks.
To Make Yourself Invisible with Adversarial Semantic Contours
In this paper, we propose Adversarial Semantic Contour (ASC), an MAP estimate of a Bayesian formulation of sparse attack with a deceived prior of object contour.
Improving Model Generalization by On-manifold Adversarial Augmentation in the Frequency Domain
To address this issue, we proposed a novel method of Augmenting data with Adversarial examples via a Wavelet module (AdvWavAug), an on-manifold adversarial data augmentation technique that is simple to implement.
GNOT: A General Neural Operator Transformer for Operator Learning
However, there are several challenges for learning operators in practical applications like the irregular mesh, multiple input functions, and complexity of the PDEs' solution.
A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking
In our benchmark, we evaluate the robustness of 55 typical deep learning models on ImageNet with diverse architectures (e. g., CNNs, Transformers) and learning algorithms (e. g., normal supervised training, pre-training, adversarial training) under numerous adversarial attacks and out-of-distribution (OOD) datasets.
A Comprehensive Survey of Continual Learning: Theory, Method and Application
To cope with real-world dynamics, an intelligent system needs to incrementally acquire, update, accumulate, and exploit knowledge throughout its lifetime.
Benchmarking Robustness of 3D Object Detection to Common Corruptions
3D object detection is an important task in autonomous driving to perceive the surroundings.
DQ-DETR: Dual Query Detection Transformer for Phrase Extraction and Grounding
As phrase extraction can be regarded as a $1$D text segmentation problem, we formulate PEG as a dual detection problem and propose a novel DQ-DETR model, which introduces dual queries to probe different features from image and text for object prediction and phrase mask prediction.
Ranked #7 on
Referring Expression Comprehension
on RefCOCO
Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications
Recent work shows that it provides potential benefits for machine learning models by incorporating the physical prior and collected data, which makes the intersection of machine learning and physics become a prevailing paradigm.
Model-based Reinforcement Learning with a Hamiltonian Canonical ODE Network
Model-based reinforcement learning usually suffers from a high sample complexity in training the world model, especially for the environments with complex dynamics.
Model-based Reinforcement Learning
reinforcement-learning
+1
ViewFool: Evaluating the Robustness of Visual Recognition to Adversarial Viewpoints
Recent studies have demonstrated that visual recognition models lack robustness to distribution shift.
A Unified Hard-Constraint Framework for Solving Geometrically Complex PDEs
We present a unified hard-constraint framework for solving geometrically complex PDEs with neural networks, where the most commonly used Dirichlet, Neumann, and Robin boundary conditions (BCs) are considered.
Offline Reinforcement Learning via High-Fidelity Generative Behavior Modeling
To address this problem, we adopt a generative approach by decoupling the learned policy into two parts: an expressive generative behavior model and an action evaluation model.
All are Worth Words: A ViT Backbone for Diffusion Models
We evaluate U-ViT in unconditional and class-conditional image generation, as well as text-to-image generation tasks, where U-ViT is comparable if not superior to a CNN-based U-Net of a similar size.
Ranked #4 on
Text-to-Image Generation
on MS COCO
Bi-level Physics-Informed Neural Networks for PDE Constrained Optimization using Broyden's Hypergradients
In this paper, we present a novel bi-level optimization framework to resolve the challenge by decoupling the optimization of the targets and constraints.
On the Reuse Bias in Off-Policy Reinforcement Learning
In this paper, we reveal that the instability is also related to a new notion of Reuse Bias of IS -- the bias in off-policy evaluation caused by the reuse of the replay buffer for evaluation and optimization.
Consistent Attack: Universal Adversarial Perturbation on Embodied Vision Navigation
Among different adversarial noises, universal adversarial perturbations (UAP), i. e., a constant image-agnostic perturbation applied on every input frame of the agent, play a critical role in Embodied Vision Navigation since they are computation-efficient and application-practical during the attack.
Towards Safe Reinforcement Learning via Constraining Conditional Value-at-Risk
Though deep reinforcement learning (DRL) has obtained substantial success, it may encounter catastrophic failures due to the intrinsic uncertainty of both transition and observation.
GSmooth: Certified Robustness against Semantic Transformations via Generalized Randomized Smoothing
Under the GSmooth framework, we present a scalable algorithm that uses a surrogate image-to-image network to approximate the complex transformation.
A Roadmap for Big Model
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
Policy Learning for Robust Markov Decision Process with a Mismatched Generative Model
Our goal is to identify a near-optimal robust policy for the perturbed testing environment, which introduces additional technical difficulties as we need to simultaneously estimate the training environment uncertainty from samples and find the worst-case perturbation for testing.
Query-Efficient Black-box Adversarial Attacks Guided by a Transfer-based Prior
However, the existing methods inevitably suffer from low attack success rates or poor query efficiency since it is difficult to estimate the gradient in a high-dimensional input space with limited information.
Controllable Evaluation and Generation of Physical Adversarial Patch on Face Recognition
It is therefore imperative to develop a framework that can enable a comprehensive evaluation of the vulnerability of face recognition in the physical world.
DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection
Compared to other models on the leaderboard, DINO significantly reduces its model size and pre-training data size while achieving better results.
Ranked #1 on
Real-Time Object Detection
on COCO 2017 val
DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR
We present in this paper a novel query formulation using dynamic anchor boxes for DETR (DEtection TRansformer) and offer a deeper understanding of the role of queries in DETR.
Ranked #11 on
2D Object Detection
on SARDet-100K
Denoised Internal Models: a Brain-Inspired Autoencoder against Adversarial Attacks
Despite its great success, deep learning severely suffers from robustness; that is, deep neural networks are very vulnerable to adversarial attacks, even the simplest ones.
Unrestricted Adversarial Attacks on ImageNet Competition
Many works have investigated the adversarial attacks or defenses under the settings where a bounded and imperceptible perturbation can be added to the input.
Adversarial Attacks on ML Defense Models Competition
Due to the vulnerability of deep neural networks (DNNs) to adversarial examples, a large number of defense techniques have been proposed to alleviate this problem in recent years.
Model-Agnostic Meta-Attack: Towards Reliable Evaluation of Adversarial Robustness
The vulnerability of deep neural networks to adversarial examples has motivated an increasing number of defense strategies for promoting model robustness.
You Cannot Easily Catch Me: A Low-Detectable Adversarial Patch for Object Detectors
Fortunately, most existing adversarial patches can be outwitted, disabled and rejected by a simple classification network called an adversarial patch detector, which distinguishes adversarial patches from original images.
Cluster Attack: Query-based Adversarial Attacks on Graphs with Graph-Dependent Priors
In this work, we propose Cluster Attack -- a Graph Injection Attack (GIA) on node classification, which injects fake nodes into the original graph to degenerate the performance of graph neural networks (GNNs) on certain victim nodes while affecting the other nodes as little as possible.
Improving the Robustness of Adversarial Attacks Using an Affine-Invariant Gradient Estimator
As designers of artificial intelligence try to outwit hackers, both sides continue to hone in on AI's inherent vulnerabilities.
Tianshou: a Highly Modularized Deep Reinforcement Learning Library
In this paper, we present Tianshou, a highly modularized Python library for deep reinforcement learning (DRL) that uses PyTorch as its backend.
Query2Label: A Simple Transformer Way to Multi-Label Classification
The use of Transformer is rooted in the need of extracting local discriminative features adaptively for different labels, which is a strongly desired property due to the existence of multiple objects in one image.
Ranked #1 on
Multi-Label Classification
on PASCAL VOC 2012
Proceedings of ICML 2021 Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI
This workshop pays a special interest in theoretic foundations, limitations, and new application trends in the scope of XAI.
Boosting Transferability of Targeted Adversarial Examples via Hierarchical Generative Networks
Transfer-based adversarial attacks can evaluate model robustness in the black-box setting.
Understanding Adversarial Attacks on Observations in Deep Reinforcement Learning
In this paper, we provide a framework to better understand the existing methods by reformulating the problem of adversarial attacks on reinforcement learning in the function space.
Accumulative Poisoning Attacks on Real-time Data
Collecting training data from untrusted sources exposes machine learning services to poisoning adversaries, who maliciously manipulate training data to degrade the model accuracy.
Exploring Memorization in Adversarial Training
In this paper, we explore the memorization effect in adversarial training (AT) for promoting a deeper understanding of model capacity, convergence, generalization, and especially robust overfitting of the adversarially trained models.
Two Coupled Rejection Metrics Can Tell Adversarial Examples Apart
Along with this routine, we find that confidence and a rectified confidence (R-Con) can form two coupled rejection metrics, which could provably distinguish wrongly classified inputs from correctly classified ones.
Unsupervised Part Segmentation through Disentangling Appearance and Shape
We study the problem of unsupervised discovery and segmentation of object parts, which, as an intermediate local representation, are capable of finding intrinsic object structure and providing more explainable recognition results.
Automated Decision-based Adversarial Attacks
Deep learning models are vulnerable to adversarial examples, which can fool a target classifier by imposing imperceptible perturbations onto natural examples.
Flexi-Transducer: Optimizing Latency, Accuracy and Compute forMulti-Domain On-Device Scenarios
In order to achieve flexible and better accuracy and latency trade-offs, the following techniques are used.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Dissecting User-Perceived Latency of On-Device E2E Speech Recognition
As speech-enabled devices such as smartphones and smart speakers become increasingly ubiquitous, there is growing interest in building automatic speech recognition (ASR) systems that can run directly on-device; end-to-end (E2E) speech recognition models such as recurrent neural network transducers and their variants have recently emerged as prime candidates for this task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
LiBRe: A Practical Bayesian Approach to Adversarial Detection
Despite their appealing flexibility, deep neural networks (DNNs) are vulnerable against adversarial examples.
Black-box Detection of Backdoor Attacks with Limited Information and Data
Although deep neural networks (DNNs) have made rapid progress in recent years, they are vulnerable in adversarial environments.
QAIR: Practical Query-efficient Black-Box Attacks for Image Retrieval
Comprehensive experiments show that the proposed attack achieves a high attack success rate with few queries against the image retrieval systems under the black-box setting.
Growth, Electronic Structure and Superconductivity of Ultrathin Epitaxial CoSi2 Films
We report growth, electronic structure and superconductivity of ultrathin epitaxial CoSi2 films on Si(111).
Mesoscale and Nanoscale Physics
Adaptive N-step Bootstrapping with Off-policy Data
In this work, we point out that the optimal value of n actually differs on each data point, while the fixed value n is a rough average of them.
Composite Adversarial Attacks
Adversarial attack is a technique for deceiving Machine Learning (ML) models, which provides a way to evaluate the adversarial robustness.
Robust Unsupervised Small Area Change Detection from SAR Imagery Using Deep Learning
In this paper, a robust unsupervised approach is proposed for small area change detection from multi-temporal SAR images using deep learning.
Alignment Restricted Streaming Recurrent Neural Network Transducer
There is a growing interest in the speech community in developing Recurrent Neural Network Transducer (RNN-T) models for automatic speech recognition (ASR) applications.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Bi-level Score Matching for Learning Energy-based Latent Variable Models
This paper presents a bi-level score matching (BiSM) method to learn EBLVMs with general structures by reformulating SM as a bi-level optimization problem.
Bag of Tricks for Adversarial Training
Adversarial training (AT) is one of the most effective strategies for promoting model robustness.
Training Interpretable Convolutional Neural Networks by Differentiating Class-specific Filters
Most existing works attempt post-hoc interpretation on a pre-trained model, while neglecting to reduce the entanglement underlying the model.
RobFR: Benchmarking Adversarial Robustness on Face Recognition
Based on large-scale evaluations, the commercial FR API services fail to exhibit acceptable performance on robustness evaluation, and we also draw several important conclusions for understanding the adversarial robustness of FR models and providing insights for the design of robust FR models.
Towards Face Encryption by Generating Adversarial Identity Masks
As billions of personal data being shared through social media and network, the data privacy and security have drawn an increasing attention.
Triple Memory Networks: a Brain-Inspired Method for Continual Learning
Continual acquisition of novel experience without interfering previously learned knowledge, i. e. continual learning, is critical for artificial neural networks, but limited by catastrophic forgetting.
A Robust Imbalanced SAR Image Change Detection Approach Based on Deep Difference Image and PCANet
Parallel FCM are utilized on these two mapped DDIs to obtain three types of pseudo-label pixels, namely, changed pixels, unchanged pixels, and intermediate pixels.
User-Level Privacy-Preserving Federated Learning: Analysis and Performance Optimization
According to our analysis, the UDP framework can realize $(\epsilon_{i}, \delta_{i})$-LDP for the $i$-th MT with adjustable privacy protection levels by varying the variances of the artificial noise processes.
Boosting Adversarial Training with Hypersphere Embedding
Adversarial training (AT) is one of the most effective defenses against adversarial attacks for deep learning models.
Adversarial Distributional Training for Robust Deep Learning
Adversarial training (AT) is among the most effective techniques to improve model robustness by augmenting training data with adversarial examples.
OoDAnalyzer: Interactive Analysis of Out-of-Distribution Samples
To better analyze and understand the OoD samples in context, we have developed a novel kNN-based grid layout algorithm motivated by Hall's theorem.
Analyzing the Noise Robustness of Deep Neural Networks
The key is to compare and analyze the datapaths of both the adversarial and normal examples.
Adversarially Robust Neural Networks via Optimal Control: Bridging Robustness with Lyapunov Stability
From this viewpoint, training neural nets is equivalent to finding an optimal control of the discrete dynamical system, which allows one to utilize methods of successive approximations, an optimal control algorithm based on Pontryagin's maximum principle, to train neural nets.
SVQN: Sequential Variational Soft Q-Learning Networks
Partially Observable Markov Decision Processes (POMDPs) are popular and flexible models for real-world decision-making applications that demand the information from past observations to make optimal decisions.
Benchmarking Adversarial Robustness
Deep neural networks are vulnerable to adversarial examples, which becomes one of the most important research problems in the development of deep learning.
Biometrics Recognition Using Deep Learning: A Survey
Deep learning-based models have been very successful in achieving state-of-the-art results in many of the computer vision, speech recognition, and natural language processing tasks in the last few years.
Interpretable Disentanglement of Neural Networks by Extracting Class-Specific Subnetwork
We also apply extracted subnetworks in visual explanation and adversarial example detection tasks by merely replacing the original full model with class-specific subnetworks.
Pruning from Scratch
Network pruning is an important research field aiming at reducing computational costs of neural networks.
Training Interpretable Convolutional Neural Networks towards Class-specific Filters
Convolutional neural networks (CNNs) have often been treated as “black-box” and successfully used in a range of tasks.
Neuromorphic Visual Odometry System for Intelligent Vehicle Application with Bio-inspired Vision Sensor
The neuromorphic camera is a brand new vision sensor that has emerged in recent years.
Robotics
Improving Black-box Adversarial Attacks with a Transfer-based Prior
We consider the black-box adversarial setting, where the adversary has to generate adversarial perturbations without access to the target models to compute gradients.
Boosting Generative Models by Leveraging Cascaded Meta-Models
Besides, our framework can be extended to semi-supervised boosting, where the boosted model learns a joint distribution of data and labels.
Sample-efficient policy learning in multi-agent Reinforcement Learning via meta-learning
We can solve these tasks by first building models for other agents and then finding the optimal policy with these models.
Pixel-Adaptive Convolutional Neural Networks
In addition, we also demonstrate that PAC can be used as a drop-in replacement for convolution layers in pre-trained networks, resulting in consistent performance improvements.
Efficient Decision-based Black-box Adversarial Attacks on Face Recognition
In this paper, we evaluate the robustness of state-of-the-art face recognition models in the decision-based black-box attack setting, where the attackers have no access to the model parameters and gradients, but can only acquire hard-label predictions by sending queries to the target model.
Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks
In this paper, we propose a translation-invariant attack method to generate more transferable adversarial examples against the defense models.
Reward Shaping via Meta-Learning
Reward shaping is one of the most effective methods to tackle the crucial yet challenging problem of credit assignment in Reinforcement Learning (RL).
Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples
3) We propose to improve the consistency of neurons on adversarial example subset by an adversarial training algorithm with a consistent loss.
Analyzing the Noise Robustness of Deep Neural Networks
Deep neural networks (DNNs) are vulnerable to maliciously generated adversarial examples.
Deep Structured Generative Models
Deep generative models have shown promising results in generating realistic images, but it is still non-trivial to generate images with complicated structures.
Open Logo Detection Challenge
In this work, we introduce a more realistic and challenging logo detection setting, called Open Logo Detection.
Interpret Neural Networks by Identifying Critical Data Routing Paths
Interpretability of a deep neural network aims to explain the rationale behind its decisions and enable the users to understand the intelligent agents, which has become an important issue due to its importance in practical applications.
Textbook Question Answering Under Instructor Guidance With Memory Networks
The machine thus performs as an instructor to extract the essay-level contradictions as the Guidance.
Robust and Efficient Graph Correspondence Transfer for Person Re-identification
To address this problem, in this paper, we present a robust and efficient graph correspondence transfer (REGCT) approach for explicit spatial alignment in Re-ID.
Graph Correspondence Transfer for Person Re-identification
In this paper, we propose a graph correspondence transfer (GCT) approach for person re-identification.
Scalable Deep Learning Logo Detection
Existing logo detection methods usually consider a small number of logo classes and limited images per class with a strong assumption of requiring tedious object bounding box annotations, therefore not scalable to real-world dynamic applications.
Weighted Bilinear Coding over Salient Body Parts for Person Re-identification
Meanwhile, a weighting scheme is applied on the bilinear coding to adaptively adjust the weights of local features at different locations based on their importance in recognition, further improving the discriminability of feature aggregation.
Sparse Adversarial Perturbations for Videos
Although adversarial samples of deep neural networks (DNNs) have been intensively studied on static images, their extensions in videos are never explored.
SPLATNet: Sparse Lattice Networks for Point Cloud Processing
We present a network architecture for processing point clouds that directly operates on a collection of points represented as a sparse set of samples in a high-dimensional lattice.
Ranked #29 on
Semantic Segmentation
on ScanNet
Understanding Human Behaviors in Crowds by Imitating the Decision-Making Process
Crowd behavior understanding is crucial yet challenging across a wide range of applications, since crowd behavior is inherently determined by a sequential decision-making process based on various factors, such as the pedestrians' own destinations, interaction with nearby pedestrians and anticipation of upcoming events.
Detecting Institutional Dialog Acts in Police Traffic Stops
We apply computational dialog methods to police body-worn camera footage to model conversations between police officers and community members in traffic stops.
Learning to Write Stylized Chinese Characters by Reading a Handful of Examples
Automatically writing stylized Chinese characters is an attractive yet challenging task due to its wide applicabilities.
Incorporating External Knowledge to Answer Open-Domain Visual Questions with Dynamic Memory Networks
To address this issue, we propose a novel framework which endows the model capabilities in answering more complex questions by leveraging massive external knowledge with dynamic memory networks.
Boosting Adversarial Attacks with Momentum
To further improve the success rates for black-box attacks, we apply momentum iterative algorithms to an ensemble of models, and show that the adversarially trained models with a strong defense ability are also vulnerable to our black-box attacks.
End-To-End Face Detection and Cast Grouping in Movies Using Erdos-Renyi Clustering
We introduce a novel verification method, rank-1 counts verification, that has this property, and use it in a link-based clustering scheme.
End-to-end Face Detection and Cast Grouping in Movies Using Erdős-Rényi Clustering
We introduce a novel verification method, rank-1 counts verification, that has this property, and use it in a link-based clustering scheme.
Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples
We find that: (1) the neurons in DNNs do not truly detect semantic objects/parts, but respond to objects/parts only as recurrent discriminative patches; (2) deep visual representations are not robust distributed codes of visual concepts because the representations of adversarial images are largely not consistent with those of real images, although they have similar visual appearance, both of which are different from previous findings.
Learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization
This procedure can greatly compensate the quantization error and thus yield better accuracy for low-bit DNNs.
SAM: Semantic Attribute Modulation for Language Modeling and Style Variation
This paper presents a Semantic Attribute Modulation (SAM) for language modeling and style variation.
Improving Interpretability of Deep Neural Networks with Semantic Information
Interpretability of deep neural networks (DNNs) is essential since it enables users to understand the overall strengths and weaknesses of the models, conveys an understanding of how the models will behave in the future, and how to diagnose and correct potential problems.
Deep Learning Logo Detection with Data Expansion by Synthesising Context
Logo detection in unconstrained images is challenging, particularly when only very sparse labelled training images are accessible due to high labelling costs.
FLASH: Fast Bayesian Optimization for Data Analytic Pipelines
To achieve the best performance, it is often critical to select optimal algorithms and to set appropriate hyperparameters, which requires large computational efforts.
Experiments on Parallel Training of Deep Neural Network using Model Averaging
Data is partitioned and distributed to different nodes for local model updates, and model averaging across nodes is done every few minibatches.
Active Sample Selection and Correction Propagation on a Gradually-Augmented Graph
When data have a complex manifold structure or the characteristics of data evolve over time, it is unrealistic to expect a graph-based semi-supervised learning method to achieve flawless classification given a small number of initial annotations.
Multi-view Convolutional Neural Networks for 3D Shape Recognition
A longstanding question in computer vision concerns the representation of 3D shapes for recognition: should 3D shapes be represented with descriptors operating on their native 3D formats, such as voxel grid or polygon mesh, or can they be effectively represented with view-based descriptors?
Ranked #95 on
3D Point Cloud Classification
on ModelNet40
