The paper discusses adversarial training’s robustness-accuracy trade-off problem and proposes a solution. The authors focus on invariance regularization to create adversarially invariant representations without losing discriminative power. They identify two…
Continue readingThe study presents InstaGen, a novel method to enhance object detector’s ability by training on synthetic dataset generated from diffusion models. The authors integrated an instance-level grounding head into a…
Continue readingThis paper presents MAPPING, a model-agnostic framework for debiasing Graph Neural Networks (GNNs) to ensure fair node classification and reduce leakage of sensitive information. Despite the success of GNNs in…
Continue readingThe article explores the possibility of AI systems learning deceptive behavior and maintaining this behavior despite safety training techniques. This is demonstrated by training models to write secure code for…
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