Adversarial Feature Alignment: Balancing Robustness and Accuracy in Deep Learning via Adversarial Training

no code implementations • 19 Feb 2024 • Leo Hyun Park, JaeUk Kim, Myung Gyo Oh, Jaewoo Park, Taekyoung Kwon

Deep learning models continue to advance in accuracy, yet they remain vulnerable to adversarial attacks, which often lead to the misclassification of adversarial examples.

Contrastive Learning Data Augmentation

LearningGroup: A Real-Time Sparse Training on FPGA via Learnable Weight Grouping for Multi-Agent Reinforcement Learning

no code implementations • 29 Oct 2022 • Je Yang, JaeUk Kim, Joo-Young Kim

Unlike supervised model or single-agent reinforcement learning, which actively exploits network pruning, it is obscure that how pruning will work in multi-agent reinforcement learning with its cooperative and interactive characteristics.

Multi-agent Reinforcement Learning Network Pruning +3