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Found 35 papers, 10 papers with code
Boosting Transferability in Vision-Language Attacks via Diversification along the Intersection Region of Adversarial Trajectory
Vision-language pre-training (VLP) models exhibit remarkable capabilities in comprehending both images and text, yet they remain susceptible to multimodal adversarial examples (AEs).
Multisize Dataset Condensation
These two challenges connect to the "subset degradation problem" in traditional dataset condensation: a subset from a larger condensed dataset is often unrepresentative compared to directly condensing the whole dataset to that smaller size.
A First-Order Multi-Gradient Algorithm for Multi-Objective Bi-Level Optimization
In this paper, we study the Multi-Objective Bi-Level Optimization (MOBLO) problem, where the upper-level subproblem is a multi-objective optimization problem and the lower-level subproblem is for scalar optimization.
IRAD: Implicit Representation-driven Image Resampling against Adversarial Attacks
The underlying rationale behind our idea is that image resampling can alleviate the influence of adversarial perturbations while preserving essential semantic information, thereby conferring an inherent advantage in defending against adversarial attacks.
SuperInpaint: Learning Detail-Enhanced Attentional Implicit Representation for Super-resolutional Image Inpainting
Given the coordinates of a pixel we want to reconstruct, we first collect its neighboring pixels in the input image and extract their detail-enhanced semantic embeddings, unmask-attentional semantic embeddings, importance values, and spatial distances to the desired pixel.
Nonparametric Iterative Machine Teaching
In this paper, we consider the problem of Iterative Machine Teaching (IMT), where the teacher provides examples to the learner iteratively such that the learner can achieve fast convergence to a target model.
Causal Intervention for Abstractive Related Work Generation
Abstractive related work generation has attracted increasing attention in generating coherent related work that better helps readers grasp the background in the current research.
Learning Restoration is Not Enough: Transfering Identical Mapping for Single-Image Shadow Removal
State-of-the-art shadow removal methods train deep neural networks on collected shadow & shadow-free image pairs, which are desired to complete two distinct tasks via shared weights, i. e., data restoration for shadow regions and identical mapping for non-shadow regions.
Leveraging Inpainting for Single-Image Shadow Removal
In this work, we find that pretraining shadow removal networks on the image inpainting dataset can reduce the shadow remnants significantly: a naive encoder-decoder network gets competitive restoration quality w. r. t.
DigNet: Digging Clues from Local-Global Interactive Graph for Aspect-level Sentiment Classification
In aspect-level sentiment classification (ASC), state-of-the-art models encode either syntax graph or relation graph to capture the local syntactic information or global relational information.
Fine-Tuning from Limited Feedbacks
Instead of learning from scratch, fine-tuning a pre-trained model to fit a related target dataset of interest or downstream tasks has been a handy trick to achieve the desired performance.
Imitation Learning: Progress, Taxonomies and Challenges
Imitation learning aims to extract knowledge from human experts' demonstrations or artificially created agents in order to replicate their behaviors.
Neural Optimization Kernel: Towards Robust Deep Learning
We further establish a new generalization bound of our deep structured approximated NOK architecture.
Contrastive Attraction and Contrastive Repulsion for Representation Learning
We realize this strategy with contrastive attraction and contrastive repulsion (CACR), which makes the query not only exert a greater force to attract more distant positive samples but also do so to repel closer negative samples.
Generative Transition Mechanism to Image-to-Image Translation via Encoded Transformation
To benefit the generalization ability of the translation model, we propose transition encoding to facilitate explicit regularization of these two {kinds} of consistencies on unseen transitions.
Human-Understandable Decision Making for Visual Recognition
The widespread use of deep neural networks has achieved substantial success in many tasks.
On the Geometry of Deep Bayesian Active Learning
To guarantee the improvements, our generalization analysis proves that, compared to typical Bayesian spherical interpretation, geodesic search with ellipsoid can derive a tighter lower error bound and achieve higher probability to obtain a nearly zero error.
Streamlining EM into Auto-Encoder Networks
We present a new deep neural network architecture, named EDGaM, for deep clustering.
A Simple Sparse Denoising Layer for Robust Deep Learning
In this work, we take an initial step to designing a simple robust layer as a lightweight plug-in for vanilla deep models.
Learning Efficient Planning-based Rewards for Imitation Learning
Our method learns useful planning computations with a meaningful reward function that focuses on the resulting region of an agent executing an action.
Towards Equivalent Transformation of User Preferences in Cross Domain Recommendation
In this paper, we attempt to learn both features of user preferences in a more principled way.
Learning Node Representations against Perturbations
In this paper, we study how to learn node representations against perturbations in GNN.
Copy and Paste GAN: Face Hallucination from Shaded Thumbnails
This paper proposes a Copy and Paste Generative Adversarial Network (CPGAN) to recover authentic high-resolution (HR) face images while compensating for low and non-uniform illumination.
MULTI-LABEL METRIC LEARNING WITH BIDIRECTIONAL REPRESENTATION DEEP NEURAL NETWORKS
We propose an extendable and end-to-end deep representation approach for metric learning on multi-label data set that is based on neural networks able to operate on feature data or directly on raw image data.
Domain-adversarial Network Alignment
Network alignment is a critical task to a wide variety of fields.
Probabilistic CCA with Implicit Distributions
Although some works have studied probabilistic interpretation for CCA, these models still require the explicit form of the distributions to achieve a tractable solution for the inference.
SIGUA: Forgetting May Make Learning with Noisy Labels More Robust
Given data with noisy labels, over-parameterized deep networks can gradually memorize the data, and fit everything in the end.
Pumpout: A Meta Approach for Robustly Training Deep Neural Networks with Noisy Labels
To handle these issues, by using the memorization effects of deep neural networks, we may train deep neural networks on the whole dataset only the first few iterations.
Canonical Correlation Analysis with Implicit Distributions
Based on this objective, we present an implicit probabilistic formulation for CCA, named Implicit CCA (ICCA), which provides a flexible framework to design CCA extensions with implicit distributions.
Masking: A New Perspective of Noisy Supervision
It is important to learn various types of classifiers given training data with noisy labels.
Ranked #42 on
Image Classification
on Clothing1M
(using extra training data)
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
Deep learning with noisy labels is practically challenging, as the capacity of deep models is so high that they can totally memorize these noisy labels sooner or later during training.
Ranked #8 on
Learning with noisy labels
on CIFAR-10N-Random3
Variational Composite Autoencoders
Learning in the latent variable model is challenging in the presence of the complex data structure or the intractable latent variable.
Sparse Embedded k-Means Clustering
For example, compared to the advanced singular value decomposition based feature extraction approach, [1] reduce the running time by a factor of $\min \{n, d\}\epsilon^2 log(d)/k$ for data matrix $X \in \mathbb{R}^{n\times d} $ with $n$ data points and $d$ features, while losing only a factor of one in approximation accuracy.
Deep Learning from Noisy Image Labels with Quality Embedding
However, the label noise among the datasets severely degenerates the \mbox{performance of deep} learning approaches.
On the Optimality of Classifier Chain for Multi-label Classification
Based on our results, we propose a dynamic programming based classifier chain (CC-DP) algorithm to search the globally optimal label order for CC and a greedy classifier chain (CC-Greedy) algorithm to find a locally optimal CC.
