Denoising Cosine Similarity: A Theory-Driven Approach for Efficient Representation Learning

no code implementations • 19 Apr 2023 • Takumi Nakagawa, Yutaro Sanada, Hiroki Waida, Yuhui Zhang, Yuichiro Wada, Kōsaku Takanashi, Tomonori Yamada, Takafumi Kanamori

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.

Denoising Representation Learning