Bootstrap Your Own Latent (BYOL) is a self-supervised learning approach for image
representation. From an augmented view of an image, BYOL trains an online network to …

Siamese networks have become a common structure in various recent models for
unsupervised visual representation learning. These models maximize the similarity between …

Abstract We introduce Bootstrap Your Own Latent (BYOL), a new approach to self-
supervised image representation learning. BYOL relies on two neural networks, referred to …

By leveraging contrastive learning, clustering, and other pretext tasks, unsupervised
methods for learning image representations have reached impressive results on standard …

This paper is concerned with self-supervised learning for small models. The problem is
motivated by our empirical studies that while the widely used contrastive self-supervised …

Contrastive learning has shown remarkable results in recent self-supervised approaches for
visual representation. By learning to contrast positive pairs' representation from the …

Despite recent progress made by self-supervised methods in representation learning with
residual networks, they still underperform supervised learning on the ImageNet classification …

This paper investigates two techniques for developing efficient self-supervised vision
transformers (EsViT) for visual representation learning. First, we show through a …

Abstract We propose Masked Siamese Networks (MSN), a self-supervised learning
framework for learning image representations. Our approach matches the representation of …

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