We launch EVA, a vision-centric foundation model to explore the limits of visual
representation at scale using only publicly accessible data. EVA is a vanilla ViT pre-trained …

Hierarchical semantic structures naturally exist in an image dataset, in which several
semantically relevant image clusters can be further integrated into a larger cluster with …

A current remarkable improvement of unsupervised visual representation learning is based
on heavy networks with large-batch training. While recent methods have greatly reduced the …

In this paper, we propose a recurrent framework for joint unsupervised learning of deep
representations and image clusters. In our framework, successive operations in a clustering …

In recent years, discriminative self-supervised methods have made significant strides in
advancing various visual tasks. The central idea of learning a data encoder that is robust to …

In contrastive self-supervised learning, the common way to learn discriminative
representation is to pull different augmented" views" of the same image closer while pushing …

Contrastive representation learning has shown to be effective to learn representations from
unlabeled data. However, much progress has been made in vision domains relying on data …

Representation learning that leverages large-scale labelled datasets, is central to recent
progress in machine learning. Access to task relevant labels at scale is often scarce or …

State-of-the-art frameworks in self-supervised learning have recently shown that fully
utilizing transformer-based models can lead to performance boost compared to conventional …

In this work, we introduce a self-supervised feature representation learning framework
DreamTeacher that utilizes generative networks for pre-training downstream image …