485 citations · 1.1k across the 52 of their papers we have counts for
52 papers
Balance, Imbalance, and Rebalance: Understanding Robust Overfitting from a Minimax Game Perspective
Yifei Wang, Liangchen Li, Jiansheng Yang +2
Adversarial Training (AT) has become arguably the state-of-the-art algorithm for extracting robust features. However, researchers recently notice that AT suffers from severe robust…
Robust Long-Tailed Learning via Label-Aware Bounded CVaR
Hong Zhu, Runpeng Yu, Xing Tang +3
Data in the real-world classification problems are always imbalanced or long-tailed, wherein the majority classes have the most of the samples that dominate the model training. In…
On the Generalization of Multi-modal Contrastive Learning
Qi Zhang, Yifei Wang, Yisen Wang
Multi-modal contrastive learning (MMCL) has recently garnered considerable interest due to its superior performance in visual tasks, achieved by embedding multi-modal data, such as…
Rethinking Weak Supervision in Helping Contrastive Learning
Jingyi Cui, Weiran Huang, Yifei Wang +1
Contrastive learning has shown outstanding performances in both supervised and unsupervised learning, and has recently been introduced to solve weakly supervised learning problems…
ContraNorm: A Contrastive Learning Perspective on Oversmoothing and Beyond
Xiaojun Guo, Yifei Wang, Tianqi Du +1
Oversmoothing is a common phenomenon in a wide range of Graph Neural Networks (GNNs) and Transformers, where performance worsens as the number of layers increases. Instead of chara…
CFA: Class-wise Calibrated Fair Adversarial Training
Zeming Wei, Yifei Wang, Yiwen Guo +1
Adversarial training has been widely acknowledged as the most effective method to improve the adversarial robustness against adversarial examples for Deep Neural Networks (DNNs). S…
Generalist: Decoupling Natural and Robust Generalization
Hongjun Wang, Yisen Wang
Deep neural networks obtained by standard training have been constantly plagued by adversarial examples. Although adversarial training demonstrates its capability to defend against…
A Message Passing Perspective on Learning Dynamics of Contrastive Learning
Yifei Wang, Qi Zhang, Tianqi Du +3
In recent years, contrastive learning achieves impressive results on self-supervised visual representation learning, but there still lacks a rigorous understanding of its learning…
Towards a Unified Theoretical Understanding of Non-contrastive Learning via Rank Differential Mechanism
Zhijian Zhuo, Yifei Wang, Jinwen Ma +1
Recently, a variety of methods under the name of non-contrastive learning (like BYOL, SimSiam, SwAV, DINO) show that when equipped with some asymmetric architectural designs, align…
Rethinking the Effect of Data Augmentation in Adversarial Contrastive Learning
Rundong Luo, Yifei Wang, Yisen Wang
Recent works have shown that self-supervised learning can achieve remarkable robustness when integrated with adversarial training (AT). However, the robustness gap between supervis…
ArCL: Enhancing Contrastive Learning with Augmentation-Robust Representations
Xuyang Zhao, Tianqi Du, Yisen Wang +2
Self-Supervised Learning (SSL) is a paradigm that leverages unlabeled data for model training. Empirical studies show that SSL can achieve promising performance in distribution shi…
Towards Memory- and Time-Efficient Backpropagation for Training Spiking Neural Networks
Qingyan Meng, Mingqing Xiao, Shen Yan +3
Spiking Neural Networks (SNNs) are promising energy-efficient models for neuromorphic computing. For training the non-differentiable SNN models, the backpropagation through time (B…
SPIDE: A Purely Spike-based Method for Training Feedback Spiking Neural Networks
Mingqing Xiao, Qingyan Meng, Zongpeng Zhang +2
Spiking neural networks (SNNs) with event-based computation are promising brain-inspired models for energy-efficient applications on neuromorphic hardware. However, most supervised…
On the Connection between Invariant Learning and Adversarial Training for Out-of-Distribution Generalization
Shiji Xin, Yifei Wang, Jingtong Su +1
Despite impressive success in many tasks, deep learning models are shown to rely on spurious features, which will catastrophically fail when generalized to out-of-distribution (OOD…
When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture
Yichuan Mo, Dongxian Wu, Yifei Wang +2
Vision Transformers (ViTs) have recently achieved competitive performance in broad vision tasks. Unfortunately, on popular threat models, naturally trained ViTs are shown to provid…
Improving Out-of-Distribution Generalization by Adversarial Training with Structured Priors
Qixun Wang, Yifei Wang, Hong Zhu +1
Deep models often fail to generalize well in test domains when the data distribution differs from that in the training domain. Among numerous approaches to address this Out-of-Dist…
How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders
Qi Zhang, Yifei Wang, Yisen Wang
Masked Autoencoders (MAE) based on a reconstruction task have risen to be a promising paradigm for self-supervised learning (SSL) and achieve state-of-the-art performance across di…
Proving Common Mechanisms Shared by Twelve Methods of Boosting Adversarial Transferability
Quanshi Zhang, Xin Wang, Jie Ren +4
Although many methods have been proposed to enhance the transferability of adversarial perturbations, these methods are designed in a heuristic manner, and the essential mechanism…
GeoSegNet: Point Cloud Semantic Segmentation via Geometric Encoder-Decoder Modeling
Chen Chen, Yisen Wang, Honghua Chen +6
Semantic segmentation of point clouds, aiming to assign each point a semantic category, is critical to 3D scene understanding.Despite of significant advances in recent years, most…
Optimization-Induced Graph Implicit Nonlinear Diffusion
Qi Chen, Yifei Wang, Yisen Wang +2
Due to the over-smoothing issue, most existing graph neural networks can only capture limited dependencies with their inherently finite aggregation layers. To overcome this limitat…
Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation
Qingyan Meng, Mingqing Xiao, Shen Yan +3
Spiking Neural Network (SNN) is a promising energy-efficient AI model when implemented on neuromorphic hardware. However, it is a challenge to efficiently train SNNs due to their n…
A Roadmap for Big Model
Sha Yuan, Hanyu Zhao, Shuai Zhao +97
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm. Researchers have achieved various outcomes in the c…
A Unified Contrastive Energy-based Model for Understanding the Generative Ability of Adversarial Training
Yifei Wang, Yisen Wang, Jiansheng Yang +1
Adversarial Training (AT) is known as an effective approach to enhance the robustness of deep neural networks. Recently researchers notice that robust models with AT have good gene…
Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation Overlap
Yifei Wang, Qi Zhang, Yisen Wang +2
Recently, contrastive learning has risen to be a promising approach for large-scale self-supervised learning. However, theoretical understanding of how it works is still unclear. I…