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Dynamic Epsilon Scheduling: A Multi-Factor Adaptive Perturbation Budget for Adversarial Training
Alan Mitkiy, James Smith, Myungseo wong +3
Adversarial training is among the most effective strategies for defending deep neural networks against adversarial examples. A key limitation of existing adversarial training appro…
Grounding Descriptions in Images informs Zero-Shot Visual Recognition
Shaunak Halbe, Junjiao Tian, K J Joseph +4
Vision-language models (VLMs) like CLIP have been cherished for their ability to perform zero-shot visual recognition on open-vocabulary concepts. This is achieved by selecting the…
Continual Diffusion with STAMINA: STack-And-Mask INcremental Adapters
James Seale Smith, Yen-Chang Hsu, Zsolt Kira +2
Recent work has demonstrated a remarkable ability to customize text-to-image diffusion models to multiple, fine-grained concepts in a sequential (i.e., continual) manner while only…
Fast Trainable Projection for Robust Fine-Tuning
Junjiao Tian, Yen-Cheng Liu, James Seale Smith +1
Robust fine-tuning aims to achieve competitive in-distribution (ID) performance while maintaining the out-of-distribution (OOD) robustness of a pre-trained model when transferring…