Although its origins date a few decades back, contrastive learning has recently gained
popularity due to its achievements in self-supervised learning, especially in computer vision …

Contrastive learning has become an important tool in learning representations from
unlabeled data mainly relying on the idea of minimizing distance between positive data …

Abstract We present Momentum Contrast (MoCo) for unsupervised visual representation
learning. From a perspective on contrastive learning as dictionary look-up, we build a …

Contrastive learning methods for unsupervised visual representation learning have reached
remarkable levels of transfer performance. We argue that the power of contrastive learning …

We propose an end-to-end deep convolutional network to simultaneously localize and rank
relative visual attributes, given only weakly-supervised pairwise image comparisons. Unlike …

In the realms of computer vision, it is evident that deep neural networks perform better in a
supervised setting with a large amount of labeled data. The representations learned with …

We present a collaborative learning method called Mutual Contrastive Learning (MCL) for
general visual representation learning. The core idea of MCL is to perform mutual interaction …

Learning rich visual representations often require training on datasets of millions of
manually annotated examples. This substantially limits the scalability of learning effective …

In this paper, we propose a genuine group-level contrastive visual representation learning
method whose linear evaluation performance on ImageNet surpasses the vanilla supervised …

Deep clustering against self-supervised learning (SSL) is a very important and promising
direction for unsupervised visual representation learning since it requires little domain …