Unsupervised image representations have significantly reduced the gap with supervised
pretraining, notably with the recent achievements of contrastive learning methods. These …

Self-supervised learning algorithms based on instance discrimination train encoders to be
invariant to pre-defined transformations of the same instance. While most methods treat …

We introduce a self-supervised learning method that focuses on beneficial properties of
representation and their abilities in generalizing to real-world tasks. The method …

This paper presents SimCLR: a simple framework for contrastive learning of visual
representations. We simplify recently proposed contrastive self-supervised learning …

Deep networks have recently achieved great success in feature learning problem on various
computer vision applications. Among different approaches in deep learning, unsupervised …

Unsupervised learning methods based on contrastive learning have drawn increasing
attention and achieved promising results. Most of them aim to learn representations invariant …

Self-supervised pre-training (SSP) employs random image transformations to generate
training data for visual representation learning. In this paper, we first present a modeling …

Self-supervised representation learning approaches have recently surpassed their
supervised learning counterparts on downstream tasks like object detection and image …

Self-supervised learning (especially contrastive learning) has attracted great interest due to
its huge potential in learning discriminative representations in an unsupervised manner …

This paper enhances image-GPT (iGPT), one of the pioneering works that introduce
autoregressive pretraining to predict the next pixels for visual representation learning. Two …