mlsanityData to 7 Oct 2026

TriBYOL: Triplet BYOL for Self-Supervised Representation Learning

arXiv 2206.03012 ↗First posted 7 Jun 2022cs.CV cs.AIcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 17 of its 31 references that are in the map. First posted on arXiv 7 Jun 2022.

Builds on

17 of 31 references.

References · 17

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  3. 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,712
  4. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  5. 2020Exploring Simple Siamese Representation Learningcited by 2,853
  6. 2018Unsupervised Representation Learning by Predicting Image Rotationscited by 1,586
  7. 2016Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzlescited by 1,379
  8. 2019Contrastive Multiview Codingcited by 1,297
  9. 2019Self-Supervised Learning of Pretext-Invariant Representationscited by 806
  10. 2020Self-supervised Learning: Generative or Contrastivecited by 693
  11. 2019Self-supervised Visual Feature Learning with Deep Neural Networks: A Surveycited by 656
  12. 2017Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning resultscited by 470
5 more
  1. 2014Deep metric learning using Triplet networkcited by 421
  2. 2018Deep Learning for Classical Japanese Literaturecited by 415
  3. 2021Understanding self-supervised Learning Dynamics without Contrastive Pairscited by 254
  4. 2020Understanding Self-supervised Learning with Dual Deep Networkscited by 62
  5. 2020Remote Sensing Image Scene Classification with Self-Supervised Paradigm under Limited Labeled Samplescited by 36

Built on by

Cited by year

Citing papers per year: 2022: 5; 2023: 1; 2024: 1; 2025: 1.

8 papers.

  1. 2022Compressed Gastric Image Generation Based on Soft-Label Dataset Distillation for Medical Data Sharingcited by 35
  2. 2022Dataset Distillation Using Parameter Pruningcited by 27
  3. 2024Importance-Aware Adaptive Dataset Distillationcited by 18
  4. 2022COVID-19 Detection Based on Self-Supervised Transfer Learning Using Chest X-Ray Imagescited by 7
  5. 2023Heterogeneous Tri-stream Clustering Networkcited by 3
  6. 2022RGMIM: Region-Guided Masked Image Modeling for Learning Meaningful Representations from X-Ray Imagescited by 1
  7. 2025Privacy-Aware Continual Self-Supervised Learning on Multi-Window Chest Computed Tomography for Domain-Shift Robustnesscited by 1
  8. 2022BYOLMed3D: Self-Supervised Representation Learning of Medical Videos using Gradient Accumulation Assisted 3D BYOL Frameworkcited by 0