TriBYOL: Triplet BYOL for Self-Supervised Representation Learning

06/07/2022
by   Guang Li, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/23/2023

Self-Supervised Image Representation Learning: Transcending Masking with Paired Image Overlay

Self-supervised learning has become a popular approach in recent years f...
research
08/19/2023

Efficient Representation Learning for Healthcare with Cross-Architectural Self-Supervision

In healthcare and biomedical applications, extreme computational require...
research
05/31/2023

Additional Positive Enables Better Representation Learning for Medical Images

This paper presents a new way to identify additional positive pairs for ...
research
11/01/2022

RGMIM: Region-Guided Masked Image Modeling for COVID-19 Detection

Self-supervised learning has developed rapidly and also advances compute...
research
10/13/2022

The Hidden Uniform Cluster Prior in Self-Supervised Learning

A successful paradigm in representation learning is to perform self-supe...
research
04/22/2023

Learning Symbolic Representations Through Joint GEnerative and DIscriminative Training

We introduce GEDI, a Bayesian framework that combines existing self-supe...
research
09/30/2021

Mining for strong gravitational lenses with self-supervised learning

We employ self-supervised representation learning to distill information...

Please sign up or login with your details

Forgot password? Click here to reset