Concurrent Discrimination and Alignment for Self-Supervised Feature Learning

TL;DR

This paper introduces a hybrid self-supervised learning method that combines discriminative and alignment approaches to improve feature representation, leading to better performance across various downstream tasks.

Contribution

It proposes a novel hybrid framework explicitly defining attraction and repulsion mechanisms, enhancing feature learning over existing methods.

Findings

Outperforms state-of-the-art self-supervised methods on nine benchmarks.

Learns more effective features for classification and semantic segmentation.

Consistently improves transfer learning results.

Abstract

Existing self-supervised learning methods learn representation by means of pretext tasks which are either (1) discriminating that explicitly specify which features should be separated or (2) aligning that precisely indicate which features should be closed together, but ignore the fact how to jointly and principally define which features to be repelled and which ones to be attracted. In this work, we combine the positive aspects of the discriminating and aligning methods, and design a hybrid method that addresses the above issue. Our method explicitly specifies the repulsion and attraction mechanism respectively by discriminative predictive task and concurrently maximizing mutual information between paired views sharing redundant information. We qualitatively and quantitatively show that our proposed model learns better features that are more effective for the diverse downstream tasks ranging from classification to semantic segmentation. Our experiments on nine established benchmarks show that the proposed model consistently outperforms the existing state-of-the-art results of self-supervised and transfer learning protocol.