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In this paper, we propose Self-Contrastive Decorrelation (SCD), aself-supervised approach. Given an input sentence, it optimizes a jointself-contrastive and decorrelation objective. Learning a representation isfacilitated by leveraging the contrast arising from the instantiation ofstandard dropout at different rates. The proposed method is conceptually simpleyet empirically powerful. It achieves comparable results with state-of-the-artmethods on multiple benchmarks without using contrastive pairs. This studyopens up avenues for efficient self-supervised learning methods that are morerobust than current contrastive methods.

SCD: Self-Contrastive Decorrelation for Sentence Embeddings