Search Results for author:
Found 154 papers, 88 papers with code
CBW: Towards Dataset Ownership Verification for Speaker Verification via Clustering-based Backdoor Watermarking
To audit and prevent the unauthorized usage of these valuable released datasets, especially in commercial or open-source scenarios, we propose a novel dataset ownership verification method.
FinTSB: A Comprehensive and Practical Benchmark for Financial Time Series Forecasting
Addressing these limitations, we propose FinTSB, a comprehensive and practical benchmark for financial time series forecasting (FinTSF).
Error-quantified Conformal Inference for Time Series
Uncertainty quantification in time series prediction is challenging due to the temporal dependence and distribution shift on sequential data.
Retrievals Can Be Detrimental: A Contrastive Backdoor Attack Paradigm on Retrieval-Augmented Diffusion Models
Subsequently, a malicious variant of contrastive learning is adopted to inject backdoors into the retriever, which builds shortcuts from triggers to the toxicity surrogates.
TimeFilter: Patch-Specific Spatial-Temporal Graph Filtration for Time Series Forecasting
Current time series forecasting methods can be broadly classified into two categories: Channel Independent (CI) and Channel Dependent (CD) strategies, both aiming to capture the complex dependencies within time series data.
Going Beyond Feature Similarity: Effective Dataset distillation based on Class-aware Conditional Mutual Information
Dataset distillation (DD) aims to minimize the time and memory consumption needed for training deep neural networks on large datasets, by creating a smaller synthetic dataset that has similar performance to that of the full real dataset.
Benchmarking Open-ended Audio Dialogue Understanding for Large Audio-Language Models
Recent advances, such as GPT-4o, have enabled LALMs in back-and-forth audio dialogues with humans.
Editable-DeepSC: Reliable Cross-Modal Semantic Communications for Facial Editing
However, the communication challenges associated with Semantic Facial Editing, one of the most important real-time CV applications on social media, still remain largely unexplored.
MambaIRv2: Attentive State Space Restoration
The Mamba-based image restoration backbones have recently demonstrated significant potential in balancing global reception and computational efficiency.
IntLoRA: Integral Low-rank Adaptation of Quantized Diffusion Models
Fine-tuning large-scale text-to-image diffusion models for various downstream tasks has yielded impressive results.
BoostAdapter: Improving Vision-Language Test-Time Adaptation via Regional Bootstrapping
The historical samples are filtered from the testing data stream and serve to extract useful information from the target distribution, while the boosting samples are drawn from regional bootstrapping and capture the knowledge of the test sample itself.
Denial-of-Service Poisoning Attacks against Large Language Models
To overcome this limitation, we propose poisoning-based DoS (P-DoS) attacks for LLMs, demonstrating that injecting a single poisoned sample designed for DoS purposes can break the output length limit.
Block-to-Scene Pre-training for Point Cloud Hybrid-Domain Masked Autoencoders
We first propose a hybrid-domain masked autoencoder consisting of an encoder and decoder belonging to the scene domain and object domain, respectively.
Towards Scalable Semantic Representation for Recommendation
With recent advances in large language models (LLMs), there has been emerging numbers of research in developing Semantic IDs based on LLMs to enhance the performance of recommendation systems.
ReFIR: Grounding Large Restoration Models with Retrieval Augmentation
In this paper, we propose an orthogonal solution called the Retrieval-augmented Framework for Image Restoration (ReFIR), which incorporates retrieved images as external knowledge to extend the knowledge boundary of existing LRMs in generating details faithful to the original scene.
CALoR: Towards Comprehensive Model Inversion Defense
Model Inversion Attacks (MIAs) aim at recovering privacy-sensitive training data from the knowledge encoded in the released machine learning models.
MIBench: A Comprehensive Framework for Benchmarking Model Inversion Attack and Defense
Model Inversion (MI) attacks aim at leveraging the output information of target models to reconstruct privacy-sensitive training data, raising critical concerns regarding the privacy vulnerabilities of Deep Neural Networks (DNNs).
TimeBridge: Non-Stationarity Matters for Long-term Time Series Forecasting
Eliminating non-stationarity is essential for avoiding spurious regressions and capturing local dependencies in short-term modeling, while preserving it is crucial for revealing long-term cointegration across variates.
3D-GP-LMVIC: Learning-based Multi-View Image Coding with 3D Gaussian Geometric Priors
Multi-view image compression is vital for 3D-related applications.
Anno-incomplete Multi-dataset Detection
Object detectors have shown outstanding performance on various public datasets.
Large Point-to-Gaussian Model for Image-to-3D Generation
Recently, image-to-3D approaches have significantly advanced the generation quality and speed of 3D assets based on large reconstruction models, particularly 3D Gaussian reconstruction models.
A Closer Look at GAN Priors: Exploiting Intermediate Features for Enhanced Model Inversion Attacks
Model Inversion (MI) attacks aim to reconstruct privacy-sensitive training data from released models by utilizing output information, raising extensive concerns about the security of Deep Neural Networks (DNNs).
CLIP-Guided Generative Networks for Transferable Targeted Adversarial Attacks
Transferable targeted adversarial attacks aim to mislead models into outputting adversary-specified predictions in black-box scenarios.
Pre-training Point Cloud Compact Model with Partial-aware Reconstruction
Firstly, the positional embedding of masked patches in the decoder results in the leakage of their central coordinates, leading to limited 3D representations.
Parameter-Efficient and Memory-Efficient Tuning for Vision Transformer: A Disentangled Approach
The synthesized query equipped with task-specific knowledge serves to extract the useful features for downstream tasks from the intermediate representations of the pre-trained model in a query-only manner.
Video Watermarking: Safeguarding Your Video from (Unauthorized) Annotations by Video-based LLMs
The advent of video-based Large Language Models (LLMs) has significantly enhanced video understanding.
DreamBench++: A Human-Aligned Benchmark for Personalized Image Generation
Personalized image generation holds great promise in assisting humans in everyday work and life due to its impressive function in creatively generating personalized content.
Hierarchical Features Matter: A Deep Exploration of GAN Priors for Improved Dataset Distillation
Dataset distillation is an emerging dataset reduction method, which condenses large-scale datasets while maintaining task accuracy.
GI-NAS: Boosting Gradient Inversion Attacks through Adaptive Neural Architecture Search
In this paper, we further exploit such implicit prior knowledge by proposing Gradient Inversion via Neural Architecture Search (GI-NAS), which adaptively searches the network and captures the implicit priors behind neural architectures.
LCM: Locally Constrained Compact Point Cloud Model for Masked Point Modeling
Considering the varying information density between masked and unmasked patches in the decoder inputs of MPM, we introduce a locally constrained Mamba-based decoder.
Pre-Trained Vision-Language Models as Partial Annotators
Pre-trained vision-language models learn massive data to model unified representations of images and natural languages, which can be widely applied to downstream machine learning tasks.
GMMFormer v2: An Uncertainty-aware Framework for Partially Relevant Video Retrieval
Given a text query, partially relevant video retrieval (PRVR) aims to retrieve untrimmed videos containing relevant moments.
Not All Prompts Are Secure: A Switchable Backdoor Attack Against Pre-trained Vision Transformers
The experiments on diverse visual recognition tasks confirm the success of our switchable backdoor attack, i. e., achieving 95%+ attack success rate, and also being hard to be detected and removed.
Adversarial Robustness for Visual Grounding of Multimodal Large Language Models
Our methods not only provide a new perspective for designing novel attacks but also serve as a strong baseline for improving the adversarial robustness for visual grounding of MLLMs.
Invertible Residual Rescaling Models
To address this issue, we propose Invertible Residual Rescaling Models (IRRM) for image rescaling by learning a bijection between a high-resolution image and its low-resolution counterpart with a specific distribution.
Boundary-aware Decoupled Flow Networks for Realistic Extreme Rescaling
Recently developed generative methods, including invertible rescaling network (IRN) based and generative adversarial network (GAN) based methods, have demonstrated exceptional performance in image rescaling.
Energy-Latency Manipulation of Multi-modal Large Language Models via Verbose Samples
For verbose videos, a frame feature diversity loss is proposed to increase the feature diversity among frames.
Weakly Supervised Deep Hyperspherical Quantization for Image Retrieval
Deep quantization methods have shown high efficiency on large-scale image retrieval.
RAT: Retrieval-Augmented Transformer for Click-Through Rate Prediction
Predicting click-through rates (CTR) is a fundamental task for Web applications, where a key issue is to devise effective models for feature interactions.
FMM-Attack: A Flow-based Multi-modal Adversarial Attack on Video-based LLMs
Despite the remarkable performance of video-based large language models (LLMs), their adversarial threat remains unexplored.
CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning
Unlike existing methods that focus on training models from a single modal of time series input, large language models (LLMs) based MTSF methods with cross-modal text and time series input have recently shown great superiority, especially with limited temporal data.
Ranked #42 on
Time Series Forecasting
on ETTh1 (336) Multivariate
Knowledge Distillation
Multivariate Time Series Forecasting
+2
MambaIR: A Simple Baseline for Image Restoration with State-Space Model
In this way, our MambaIR takes advantage of the local pixel similarity and reduces the channel redundancy.
Ranked #17 on
Image Super-Resolution
on Set14 - 4x upscaling
Privacy Leakage on DNNs: A Survey of Model Inversion Attacks and Defenses
However, Model Inversion (MI) attacks, which disclose private information about the training dataset by abusing access to the trained models, have emerged as a formidable privacy threat.
An Efficient Implicit Neural Representation Image Codec Based on Mixed Autoregressive Model for Low-Complexity Decoding
To the best of our knowledge, our method is the first INR-based codec comparable with Hyperprior in both decoding speed and quality while maintaining low complexity.
Inducing High Energy-Latency of Large Vision-Language Models with Verbose Images
Once attackers maliciously induce high energy consumption and latency time (energy-latency cost) during inference of VLMs, it will exhaust computational resources.
MB-RACS: Measurement-Bounds-based Rate-Adaptive Image Compressed Sensing Network
In this paper, we propose a Measurement-Bounds-based Rate-Adaptive Image Compressed Sensing Network (MB-RACS) framework, which aims to adaptively determine the sampling rate for each image block in accordance with traditional measurement bounds theory.
Not All Prompts Are Secure: A Switchable Backdoor Attack Against Pre-trained Vision Transfomers
The experiments on diverse visual recognition tasks confirm the success of our switchable backdoor attack i. e. achieving 95%+ attack success rate and also being hard to be detected and removed.
Towards Compact 3D Representations via Point Feature Enhancement Masked Autoencoders
Specifically, to learn more compact features, a share-parameter Transformer encoder is introduced to extract point features from the global and local unmasked patches obtained by global random and local block mask strategies, followed by a specific decoder to reconstruct.
Ranked #5 on
Few-Shot 3D Point Cloud Classification
on ModelNet40 10-way (10-shot)
(using extra training data)
Parameter Efficient Adaptation for Image Restoration with Heterogeneous Mixture-of-Experts
In this work, we introduce an alternative solution to improve the generalization of image restoration models.
Towards Sample-specific Backdoor Attack with Clean Labels via Attribute Trigger
We argue that the intensity constraint of existing SSBAs is mostly because their trigger patterns are `content-irrelevant' and therefore act as `noises' for both humans and DNNs.
BadCLIP: Trigger-Aware Prompt Learning for Backdoor Attacks on CLIP
Contrastive Vision-Language Pre-training, known as CLIP, has shown promising effectiveness in addressing downstream image recognition tasks.
Progressive Learning with Visual Prompt Tuning for Variable-Rate Image Compression
In this paper, we propose a progressive learning paradigm for transformer-based variable-rate image compression.
Perceptual Image Compression with Cooperative Cross-Modal Side Information
The explosion of data has resulted in more and more associated text being transmitted along with images.
GMMFormer: Gaussian-Mixture-Model Based Transformer for Efficient Partially Relevant Video Retrieval
Current PRVR methods adopt scanning-based clip construction to achieve explicit clip modeling, which is information-redundant and requires a large storage overhead.
WFTNet: Exploiting Global and Local Periodicity in Long-term Time Series Forecasting
In this paper, we propose a Wavelet-Fourier Transform Network (WFTNet) for long-term time series forecasting.
Towards Robust Model Watermark via Reducing Parametric Vulnerability
To protect the copyright of DNNs, backdoor-based ownership verification becomes popular recently, in which the model owner can watermark the model by embedding a specific backdoor behavior before releasing it.
MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation
We propose MISSRec, a multi-modal pre-training and transfer learning framework for SR. On the user side, we design a Transformer-based encoder-decoder model, where the contextual encoder learns to capture the sequence-level multi-modal user interests while a novel interest-aware decoder is developed to grasp item-modality-interest relations for better sequence representation.
One-bit Flip is All You Need: When Bit-flip Attack Meets Model Training
We propose a training-assisted bit flip attack, in which the adversary is involved in the training stage to build a high-risk model to release.
GIFD: A Generative Gradient Inversion Method with Feature Domain Optimization
Federated Learning (FL) has recently emerged as a promising distributed machine learning framework to preserve clients' privacy, by allowing multiple clients to upload the gradients calculated from their local data to a central server.
One-stage Low-resolution Text Recognition with High-resolution Knowledge Transfer
Current solutions for low-resolution text recognition (LTR) typically rely on a two-stage pipeline that involves super-resolution as the first stage followed by the second-stage recognition.
Towards Robust Scene Text Image Super-resolution via Explicit Location Enhancement
Scene text image super-resolution (STISR), aiming to improve image quality while boosting downstream scene text recognition accuracy, has recently achieved great success.
TVTSv2: Learning Out-of-the-box Spatiotemporal Visual Representations at Scale
We argue that tuning a text encoder end-to-end, as done in previous work, is suboptimal since it may overfit in terms of styles, thereby losing its original generalization ability to capture the semantics of various language registers.
Vision-Language Pre-training with Object Contrastive Learning for 3D Scene Understanding
In recent years, vision language pre-training frameworks have made significant progress in natural language processing and computer vision, achieving remarkable performance improvement on various downstream tasks.
Backdoor Attack with Sparse and Invisible Trigger
More importantly, it is not feasible to simply combine existing methods to design an effective sparse and invisible backdoor attack.
Learnable Behavior Control: Breaking Atari Human World Records via Sample-Efficient Behavior Selection
The exploration problem is one of the main challenges in deep reinforcement learning (RL).
Ranked #1 on
Atari Games
on Atari-57
Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models
To conquer this limitation, we propose a novel Instance-aware Dynamic Prompt Tuning (IDPT) strategy for pre-trained point cloud models.
3D Parameter-Efficient Fine-Tuning for Classification
Few-Shot 3D Point Cloud Classification
+1
Unsupervised Anomaly Detection with Local-Sensitive VQVAE and Global-Sensitive Transformers
In this work, the VQVAE focus on feature extraction and reconstruction of images, and the transformers fit the manifold and locate anomalies in the latent space.
Backdoor Defense via Adaptively Splitting Poisoned Dataset
With the split clean data pool and polluted data pool, ASD successfully defends against backdoor attacks during training.
Delving into Identify-Emphasize Paradigm for Combating Unknown Bias
For challenge B, we point out that the gradient contribution statistics can be a reliable indicator to inspect whether the optimization is dominated by bias-aligned samples.
BackdoorBox: A Python Toolbox for Backdoor Learning
Third-party resources ($e. g.$, samples, backbones, and pre-trained models) are usually involved in the training of deep neural networks (DNNs), which brings backdoor attacks as a new training-phase threat.
DELTA: degradation-free fully test-time adaptation
However, we find that two unfavorable defects are concealed in the prevalent adaptation methodologies like test-time batch normalization (BN) and self-learning.
Unsupervised Surface Anomaly Detection with Diffusion Probabilistic Model
Unsupervised surface anomaly detection aims at discovering and localizing anomalous patterns using only anomaly-free training samples.
Backdoor Defense via Suppressing Model Shortcuts
Recent studies have demonstrated that deep neural networks (DNNs) are vulnerable to backdoor attacks during the training process.
Untargeted Backdoor Attack against Object Detection
The backdoored model has promising performance in predicting benign samples, whereas its predictions can be maliciously manipulated by adversaries based on activating its backdoors with pre-defined trigger patterns.
BATT: Backdoor Attack with Transformation-based Triggers
The backdoor adversaries intend to maliciously control the predictions of attacked DNNs by injecting hidden backdoors that can be activated by adversary-specified trigger patterns during the training process.
Controller-Guided Partial Label Consistency Regularization with Unlabeled Data
Partial label learning (PLL) learns from training examples each associated with multiple candidate labels, among which only one is valid.
Towards Effective Image Manipulation Detection with Proposal Contrastive Learning
Most existing methods mainly focus on extracting global features from tampered images, while neglecting the relationships of local features between tampered and authentic regions within a single tampered image.
Learning Transferable Spatiotemporal Representations from Natural Script Knowledge
Pre-training on large-scale video data has become a common recipe for learning transferable spatiotemporal representations in recent years.
Vertical Semi-Federated Learning for Efficient Online Advertising
Specifically, we build an inference-efficient single-party student model applicable to the whole sample space and meanwhile maintain the advantage of the federated feature extension.
Untargeted Backdoor Watermark: Towards Harmless and Stealthy Dataset Copyright Protection
In this paper, we revisit dataset ownership verification.
Learned Distributed Image Compression with Multi-Scale Patch Matching in Feature Domain
Specifically, MSFDPM consists of a side information feature extractor, a multi-scale feature domain patch matching module, and a multi-scale feature fusion network.
Imperceptible and Robust Backdoor Attack in 3D Point Cloud
Existing attacks often insert some additional points into the point cloud as the trigger, or utilize a linear transformation (e. g., rotation) to construct the poisoned point cloud.
Adaptive Local Implicit Image Function for Arbitrary-scale Super-resolution
Image representation is critical for many visual tasks.
Black-box Dataset Ownership Verification via Backdoor Watermarking
The rapid development of DNNs has benefited from the existence of some high-quality datasets ($e. g.$, ImageNet), which allow researchers and developers to easily verify the performance of their methods.
MOVE: Effective and Harmless Ownership Verification via Embedded External Features
In general, we conduct the ownership verification by verifying whether a suspicious model contains the knowledge of defender-specified external features.
Hardly Perceptible Trojan Attack against Neural Networks with Bit Flips
The security of deep neural networks (DNNs) has attracted increasing attention due to their widespread use in various applications.
Versatile Weight Attack via Flipping Limited Bits
Utilizing the latest technique in integer programming, we equivalently reformulate this MIP problem as a continuous optimization problem, which can be effectively and efficiently solved using the alternating direction method of multipliers (ADMM) method.
Open-Vocabulary Multi-Label Classification via Multi-Modal Knowledge Transfer
Specifically, our method exploits multi-modal knowledge of image-text pairs based on a vision and language pre-training (VLP) model.
Ranked #1 on
Multi-label zero-shot learning
on Open Images V4
VFed-SSD: Towards Practical Vertical Federated Advertising
As an emerging secure learning paradigm in lever-aging cross-agency private data, vertical federatedlearning (VFL) is expected to improve advertising models by enabling the joint learning of complementary user attributes privately owned by the advertiser and the publisher.
A Comparative Study of Feature Expansion Unit for 3D Point Cloud Upsampling
Recently, deep learning methods have shown great success in 3D point cloud upsampling.
Improving Vision Transformers by Revisiting High-frequency Components
Inspired by this finding, we first investigate the effects of existing techniques for improving ViT models from a new frequency perspective, and find that the success of some techniques (e. g., RandAugment) can be attributed to the better usage of the high-frequency components.
Ranked #2 on
Domain Generalization
on Stylized-ImageNet
Adaptive Frequency Learning in Two-branch Face Forgery Detection
Face forgery has attracted increasing attention in recent applications of computer vision.
On the Effectiveness of Adversarial Training against Backdoor Attacks
To explore whether adversarial training could defend against backdoor attacks or not, we conduct extensive experiments across different threat models and perturbation budgets, and find the threat model in adversarial training matters.
Hybrid Contrastive Quantization for Efficient Cross-View Video Retrieval
By performing Asymmetric-Quantized Contrastive Learning (AQ-CL) across views, HCQ aligns texts and videos at coarse-grained and multiple fine-grained levels.
Few-Shot Backdoor Attacks on Visual Object Tracking
Visual object tracking (VOT) has been widely adopted in mission-critical applications, such as autonomous driving and intelligent surveillance systems.
Defending against Model Stealing via Verifying Embedded External Features
In this paper, we explore the defense from another angle by verifying whether a suspicious model contains the knowledge of defender-specified \emph{external features}.
Clustering Effect of Adversarial Robust Models
Adversarial robustness has received increasing attention along with the study of adversarial examples.
Combating Unknown Bias with Effective Bias-Conflicting Scoring and Gradient Alignment
For challenge B, we point out that the gradient contribution statistics can be a reliable indicator to inspect whether the optimization is dominated by bias-aligned samples.
Clustering Effect of (Linearized) Adversarial Robust Models
Adversarial robustness has received increasing attention along with the study of adversarial examples.
Attend to Who You Are: Supervising Self-Attention for Keypoint Detection and Instance-Aware Association
This paper presents a new method to solve keypoint detection and instance association by using Transformer.
Ranked #10 on
Multi-Person Pose Estimation
on MS COCO
Deep Dirichlet Process Mixture Models
In this paper we propose the deep Dirichlet process mixture (DDPM) model, which is an unsupervised method that simultaneously performs clustering and feature learning.
Does Adversarial Robustness Really Imply Backdoor Vulnerability?
Based on thorough experiments, we find that such trade-off ignores the interactions between the perturbation budget of adversarial training and the magnitude of the backdoor trigger.
Backdoor Attack on Hash-based Image Retrieval via Clean-label Data Poisoning
To this end, we propose the confusing perturbations-induced backdoor attack (CIBA).
Contrastive Quantization with Code Memory for Unsupervised Image Retrieval
The high efficiency in computation and storage makes hashing (including binary hashing and quantization) a common strategy in large-scale retrieval systems.
PHPQ: Pyramid Hybrid Pooling Quantization for Efficient Fine-Grained Image Retrieval
To improve fine-grained image hashing, we propose Pyramid Hybrid Pooling Quantization (PHPQ).
SimCC: a Simple Coordinate Classification Perspective for Human Pose Estimation
The 2D heatmap-based approaches have dominated Human Pose Estimation (HPE) for years due to high performance.
Universal Adversarial Head: Practical Protection against Video Data Leakage
We propose $universal \ adversarial \ head$ (UAH), which crafts adversarial query videos by prepending the original videos with a sequence of adversarial frames to perturb the normal hash codes in the Hamming space.
Learnable Hypergraph Laplacian for Hypergraph Learning
Hypergraph Convolutional Neural Networks (HGCNNs) have demonstrated their potential in modeling high-order relations preserved in graph-structured data.
Learnable Hypergraph Laplacian for Hypergraph Learning
HyperGraph Convolutional Neural Networks (HGCNNs) have demonstrated their potential in modeling high-order relations preserved in graph structured data.
TokenPose: Learning Keypoint Tokens for Human Pose Estimation
Most existing CNN-based methods do well in visual representation, however, lacking in the ability to explicitly learn the constraint relationships between keypoints.
Backdoor Attack in the Physical World
We demonstrate that this attack paradigm is vulnerable when the trigger in testing images is not consistent with the one used for training.
Improving Adversarial Robustness via Channel-wise Activation Suppressing
The study of adversarial examples and their activation has attracted significant attention for secure and robust learning with deep neural networks (DNNs).
Hidden Backdoor Attack against Semantic Segmentation Models
Deep neural networks (DNNs) are vulnerable to the \emph{backdoor attack}, which intends to embed hidden backdoors in DNNs by poisoning training data.
Targeted Attack against Deep Neural Networks via Flipping Limited Weight Bits
By utilizing the latest technique in integer programming, we equivalently reformulate this BIP problem as a continuous optimization problem, which can be effectively and efficiently solved using the alternating direction method of multipliers (ADMM) method.
Stochastic Deep Gaussian Processes over Graphs
In this paper we propose Stochastic Deep Gaussian Processes over Graphs (DGPG), which are deep structure models that learn the mappings between input and output signals in graph domains.
Backdoor Attack against Speaker Verification
We also demonstrate that existing backdoor attacks cannot be directly adopted in attacking speaker verification.
JSRT: James-Stein Regression Tree
To address this issue, we propose a novel regression tree, named James-Stein Regression Tree (JSRT) by considering global information from different nodes.
DPAttack: Diffused Patch Attacks against Universal Object Detection
Recently, deep neural networks (DNNs) have been widely and successfully used in Object Detection, e. g.
Open-sourced Dataset Protection via Backdoor Watermarking
Based on the proposed backdoor-based watermarking, we use a hypothesis test guided method for dataset verification based on the posterior probability generated by the suspicious third-party model of the benign samples and their correspondingly watermarked samples ($i. e.$, images with trigger) on the target class.
Improving Query Efficiency of Black-box Adversarial Attack
Deep neural networks (DNNs) have demonstrated excellent performance on various tasks, however they are under the risk of adversarial examples that can be easily generated when the target model is accessible to an attacker (white-box setting).
Rectified Decision Trees: Exploring the Landscape of Interpretable and Effective Machine Learning
Interpretability and effectiveness are two essential and indispensable requirements for adopting machine learning methods in reality.
Neural Network-based Automatic Factor Construction
This paper proposes Neural Network-based Automatic Factor Construction (NNAFC), a tailored neural network framework that can automatically construct diversified financial factors based on financial domain knowledge and a variety of neural network structures.
Backdoor Learning: A Survey
Backdoor attack intends to embed hidden backdoor into deep neural networks (DNNs), so that the attacked models perform well on benign samples, whereas their predictions will be maliciously changed if the hidden backdoor is activated by attacker-specified triggers.
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.
Temporal Calibrated Regularization for Robust Noisy Label Learning
Deep neural networks (DNNs) exhibit great success on many tasks with the help of large-scale well annotated datasets.
Targeted Attack for Deep Hashing based Retrieval
In this paper, we propose a novel method, dubbed deep hashing targeted attack (DHTA), to study the targeted attack on such retrieval.
Adversarial Weight Perturbation Helps Robust Generalization
The study on improving the robustness of deep neural networks against adversarial examples grows rapidly in recent years.
Rethinking the Trigger of Backdoor Attack
Backdoor attack intends to inject hidden backdoor into the deep neural networks (DNNs), such that the prediction of the infected model will be maliciously changed if the hidden backdoor is activated by the attacker-defined trigger, while it performs well on benign samples.
Matrix Smoothing: A Regularization for DNN with Transition Matrix under Noisy Labels
Probabilistic modeling, which consists of a classifier and a transition matrix, depicts the transformation from true labels to noisy labels and is a promising approach.
Toward Adversarial Robustness via Semi-supervised Robust Training
In this work, we propose a novel defense method, the robust training (RT), by jointly minimizing two separated risks ($R_{stand}$ and $R_{rob}$), which is with respect to the benign example and its neighborhoods respectively.
Adversarial Attack on Deep Product Quantization Network for Image Retrieval
Deep product quantization network (DPQN) has recently received much attention in fast image retrieval tasks due to its efficiency of encoding high-dimensional visual features especially when dealing with large-scale datasets.
An Accuracy-Lossless Perturbation Method for Defending Privacy Attacks in Federated Learning
However, the strong defence ability and high learning accuracy of these schemes cannot be ensured at the same time, which will impede the wide application of FL in practice (especially for medical or financial institutions that require both high accuracy and strong privacy guarantee).
Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets
We find that using more gradients from the skip connections rather than the residual modules according to a decay factor, allows one to craft adversarial examples with high transferability.
Alpha Discovery Neural Network based on Prior Knowledge
This paper proposes Alpha Discovery Neural Network (ADNN), a tailored neural network structure which can automatically construct diversified financial technical indicators based on prior knowledge.
Automatic Financial Feature Construction
According to neural network universal approximation theorem, pre-training can conduct a more effective and explainable evolution process.
Maintaining Discrimination and Fairness in Class Incremental Learning
In this paper, we demonstrate it can indeed help the model to output more discriminative results within old classes.
Ranked #2 on
Incremental Learning
on ImageNet100 - 10 steps
(# M Params metric)
Visual Privacy Protection via Mapping Distortion
To a large extent, the privacy of visual classification data is mainly in the mapping between the image and its corresponding label, since this relation provides a great amount of information and can be used in other scenarios.
Deep Flow Collaborative Network for Online Visual Tracking
The deep learning-based visual tracking algorithms such as MDNet achieve high performance leveraging to the feature extraction ability of a deep neural network.
Adversarial Defense via Local Flatness Regularization
In this paper, we define the local flatness of the loss surface as the maximum value of the chosen norm of the gradient regarding to the input within a neighborhood centered on the benign sample, and discuss the relationship between the local flatness and adversarial vulnerability.
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.
AdaCompress: Adaptive Compression for Online Computer Vision Services
Then we train the agent in a reinforcement learning way to adapt it for different deep learning cloud services that act as the {\em interactive training environment} and feeding a reward with comprehensive consideration of accuracy and data size.
Multimedia Image and Video Processing
Adaptive Regularization of Labels
In addition, label regularization techniques such as label smoothing and label disturbance have also been proposed with the motivation of adding a stochastic perturbation to labels.
$t$-$k$-means: A Robust and Stable $k$-means Variant
$k$-means algorithm is one of the most classical clustering methods, which has been widely and successfully used in signal processing.
Self-Paced Probabilistic Principal Component Analysis for Data with Outliers
There is a probabilistic version of PCA, known as Probabilistic PCA (PPCA).
Rectified Decision Trees: Towards Interpretability, Compression and Empirical Soundness
How to obtain a model with good interpretability and performance has always been an important research topic.
Multinomial Random Forest: Toward Consistency and Privacy-Preservation
Despite the impressive performance of random forests (RF), its theoretical properties have not been thoroughly understood.
BML: A High-performance, Low-cost Gradient Synchronization Algorithm for DML Training
In distributed machine learning (DML), the network performance between machines significantly impacts the speed of iterative training.
Exploiting Common Characters in Chinese and Japanese to Learn Cross-Lingual Word Embeddings via Matrix Factorization
Learning vector space representation of words (i. e., word embeddings) has recently attracted wide research interests, and has been extended to cross-lingual scenario.
Dimensionality-Driven Learning with Noisy Labels
Datasets with significant proportions of noisy (incorrect) class labels present challenges for training accurate Deep Neural Networks (DNNs).
Ranked #41 on
Image Classification
on mini WebVision 1.0
Iterative Learning with Open-set Noisy Labels
We refer to this more complex scenario as the \textbf{open-set noisy label} problem and show that it is nontrivial in order to make accurate predictions.
Nonextensive information theoretical machine
In this paper, we propose a new discriminative model named \emph{nonextensive information theoretical machine (NITM)} based on nonextensive generalization of Shannon information theory.
Bayesian linear regression with Student-t assumptions
In this paper, we propose a Bayesian linear regression model with Student-t assumptions (BLRS), which can be inferred exactly.
Unifying Decision Trees Split Criteria Using Tsallis Entropy
In this paper, a Tsallis Entropy Criterion (TEC) algorithm is proposed to unify Shannon entropy, Gain Ratio and Gini index, which generalizes the split criteria of decision trees.
