In existing visual representation learning tasks, deep convolutional neural networks (CNNs)
are often trained on images annotated with single tag, such as ImageNet. However, single …

Contrastive learning has been adopted as a core method for unsupervised visual
representation learning. Without human annotation, the common practice is to perform an …

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

Supervised (pre-) training currently yields state-of-the-art performance for representation
learning for visual recognition, yet it comes at the cost of (1) intensive manual annotations …

Contrastive learning is a self-supervised representation learning method that achieves
milestone performance in various classification tasks. However, due to its unsupervised …

Convolution is a central operation in Convolutional Neural Networks (CNNs), which applies
a kernel to overlapping regions shifted across the image. However, because of the strong …

We propose a self-supervised learning method to uncover the spatial or temporal structure
of visual data by identifying the position of a patch within an image or the position of a video …

Recently, there have been tremendous efforts in developing lightweight Deep Neural
Networks (DNNs) with satisfactory accuracy, which can enable the ubiquitous deployment of …

Contrastive self-supervised learning has largely narrowed the gap to supervised pre-training
on ImageNet. However, its success highly relies on the object-centric priors of ImageNet, ie …

Fine-tuning pre-trained deep networks is a practical way of benefiting from the
representation learned on a large database while having relatively few examples to train a …