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Robust VAEs via Generating Process of Noise Augmented Data
Advancing defensive mechanisms against adversarial attacks in generative models is a critical research topic in machine learning.
Denoising Cosine Similarity: A Theory-Driven Approach for Efficient Representation Learning
To this end, inspired by recent works on denoising and the success of the cosine-similarity-based objective functions in representation learning, we propose the denoising Cosine-Similarity (dCS) loss.
Towards Understanding the Mechanism of Contrastive Learning via Similarity Structure: A Theoretical Analysis
We first prove that the formulation characterizes the structure of representations learned with the kernel-based contrastive learning framework.
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