Momentum Contrast for Unsupervised Visual Representation Learning
Kaiming He,Haoqi Fan,Yuxin Wu,Saining Xie,Ross Girshick +4 more
- pp 9729-9738
TLDR
This article proposed Momentum Contrast (MoCo) for unsupervised visual representation learning, which enables building a large and consistent dictionary on-the-fly that facilitates contrastive learning.Abstract:
We present Momentum Contrast (MoCo) for unsupervised visual representation learning. From a perspective on contrastive learning as dictionary look-up, we build a dynamic dictionary with a queue and a moving-averaged encoder. This enables building a large and consistent dictionary on-the-fly that facilitates contrastive unsupervised learning. MoCo provides competitive results under the common linear protocol on ImageNet classification. More importantly, the representations learned by MoCo transfer well to downstream tasks. MoCo can outperform its supervised pre-training counterpart in 7 detection/segmentation tasks on PASCAL VOC, COCO, and other datasets, sometimes surpassing it by large margins. This suggests that the gap between unsupervised and supervised representation learning has been largely closed in many vision tasks.read more
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Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning
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TL;DR: This work introduces Bootstrap Your Own Latent (BYOL), a new approach to self-supervised image representation learning that performs on par or better than the current state of the art on both transfer and semi- supervised benchmarks.
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Improved Baselines with Momentum Contrastive Learning
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Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
TL;DR: This paper proposes an online algorithm, SwAV, that takes advantage of contrastive methods without requiring to compute pairwise comparisons, and uses a swapped prediction mechanism where it predicts the cluster assignment of a view from the representation of another view.
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