Adversarial Loss

Adversarial loss is a training technique that enhances model robustness by incorporating an adversarial component into the learning process. Current research focuses on applying adversarial loss to diverse machine learning problems, including improving the accuracy and robustness of deep neural networks, mitigating overfitting in generative models, and ensuring fairness in representation learning. This approach is significant because it addresses vulnerabilities in machine learning models to adversarial attacks and improves generalization performance across various domains, leading to more reliable and trustworthy AI systems. The development of efficient algorithms for optimizing adversarial loss, particularly in complex settings like reinforcement learning and continual learning, remains a key area of investigation.

Papers

December 4, 2023

Few-Shot Anomaly Detection with Adversarial Loss for Robust Feature Representations
Jae Young Lee, Wonjun Lee, Jaehyun Choi, Yongkwi Lee, Young Seog Yoon
Anomaly Detection Adversarial Loss Shot Anomaly Detection Robust Feature Representation

November 30, 2023

SMaRt: Improving GANs with Score Matching Regularity
Mengfei Xia, Yujun Shen, Ceyuan Yang, Ran Yi, Wenping Wang, Yong-jin Liu
Generative Adversarial Network Adversarial Loss GAN Training Score Matching

November 14, 2023

Learning Adversarial Low-rank Markov Decision Processes with Unknown Transition and Full-information Feedback
Canzhe Zhao, Ruofeng Yang, Baoxiang Wang, Xuezhou Zhang, Shuai Li
Low Rank Sublinear Regret Adversarial Loss Adversarial Markov Decision Process Transition Uncertainty

November 1, 2023

Flooding Regularization for Stable Training of Generative Adversarial Networks
Iu Yahiro, Takashi Ishida, Naoto Yokoya
Generative Adversarial Network Adversarial Loss GAN Training Stable Training

October 30, 2023

Towards Practical Non-Adversarial Distribution Matching
Ziyu Gong, Ben Usman, Han Zhao, David I. Inouye
Adversarial Loss Distribution Matching Adversarial Estimator Adversarial Matching Method

October 24, 2023

Confounder Balancing in Adversarial Domain Adaptation for Pre-Trained Large Models Fine-Tuning
Shuoran Jiang, Qingcai Chen, Yang Xiang, Youcheng Pan, Xiangping Wu
Large Model Large Pre Trained Model Domain Invariant Feature Adversarial Loss Unobserved Confounders Adversarial Domain Adaptation Domain Classifier

October 17, 2023

Towards Optimal Regret in Adversarial Linear MDPs with Bandit Feedback
Haolin Liu, Chen-Yu Wei, Julian Zimmert
Optimal Regret Bandit Feedback Online Reinforcement Learning Adversarial Loss Adversarial Markov Decision Process

September 18, 2023

Reducing Adversarial Training Cost with Gradient Approximation
Huihui Gong
Adversarial Example Adversarial Training Adversarial Loss Gradient Approximation Projected Gradient Descent Efficient Adversarial Training

September 16, 2023

Music Generation based on Generative Adversarial Networks with Transformer
Ziyi Jiang, Ruoxue Wu, Zhenghan Chen, Xiaoxuan Liang
Generative Adversarial Network Transformer Based Autoregressive Model Music Generation Adversarial Loss

September 12, 2023

Distributionally Robust Transfer Learning
Xin Xiong, Zijian Guo, Tianxi Cai
Transfer Learning Adversarial Loss Distributionally Robust Optimization Distributionally Robust Distributional Robustness

September 2, 2023

August 16, 2023

July 30, 2023

On Neural Network approximation of ideal adversarial attack and convergence of adversarial training
Rajdeep Haldar, Qifan Song
Adversarial Attack Adversarial Training Early Stage Convergence Adversarial Loss Learned Function Neural Network Approximation Optimal Attack

July 27, 2023

Adversarial Sleeping Bandit Problems with Multiple Plays: Algorithm and Ranking Application
Jianjun Yuan, Wei Lee Woon, Ludovik Coba
Practical Algorithm Adversarial Text Bandit Algorithm Adversarial Loss Play Style Bandit Problem First Stage Ranker Arm Selection Online Recommender System

July 12, 2023

Single-Class Target-Specific Attack against Interpretable Deep Learning Systems
Eldor Abdukhamidov, Mohammed Abuhamad, George K. Thiruvathukal, Hyoungshick Kim, Tamer Abuhmed
Adversarial Attack Adversarial Sample Adversarial Loss Interpretable Deep Learning Targeted Attack

June 27, 2023

DSRM: Boost Textual Adversarial Training with Distribution Shift Risk Minimization
Songyang Gao, Shihan Dou, Yan Liu, Xiao Wang, Qi Zhang, Zhongyu Wei, Jin Ma, Ying Shan
Language Model Adversarial Training Distribution Shift Adversarial Loss Textual Adversarial Attack

June 25, 2023

Feature Adversarial Distillation for Point Cloud Classification
YuXing Lee, Wei Wu
Point Cloud Classification Adversarial Loss Adversarial Distillation LiDAR Distillation

June 13, 2023

On Achieving Optimal Adversarial Test Error
Justin D. Li, Matus Telgarsky
Adversarial Loss Adversarial Testing Optimal Adversarial