TriBYOL: Triplet BYOL for Self-Supervised Representation Learning

Abstract

This paper proposes a novel self-supervised learning method for learning better representations with small batch sizes. Many self-supervised learning methods based on certain forms of the siamese network have emerged and received significant attention. However, these methods need to use large batch sizes to learn good representations and require heavy computational resources. We present a new triplet network combined with a triple-view loss to improve the performance of self-supervised representation learning with small batch sizes. Experimental results show that our method can drastically outperform state-of-the-art self-supervised learning methods on several datasets in small-batch cases. Our method provides a feasible solution for self-supervised learning with real-world high-resolution images that uses small batch sizes.

Published as a conference paper at ICASSP 2022

References in corpus (5)

• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
• Bootstrap your own latent: A new approach to self-supervised Learning
• Understanding Self-supervised Learning with Dual Deep Networks
• Self-Knowledge Distillation based Self-Supervised Learning for Covid-19 Detection from Chest X-Ray Images
• Self-Supervised Learning for Gastritis Detection with Gastric X-ray Images

Cited by in corpus (1)

• Self-Knowledge Distillation based Self-Supervised Learning for Covid-19 Detection from Chest X-Ray Images