We present a principled approach to uncover the structure of visual data by solving a deep
learning task coined visual permutation learning. The goal of this task is to find the …

We present a principled approach to uncover the structure of visual data by solving a novel
deep learning task coined visual permutation learning. The goal of this task is to find the …

ShuffleNet is a state-of-the-art light weight convolutional neural network architecture. Its
basic operations include group, channel-wise convolution and channel shuffling. However …

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

We develop an approach to learning visual representations that embraces multimodal data,
driven by a combination of intra-and inter-modal similarity preservation objectives. Unlike …

Self-supervised representation learning for visual pre-training has achieved remarkable
success with sample (instance or pixel) discrimination and semantics discovery of instance …

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 …

Transfer learning has attracted a lot of attention in the past decade. One crucial research
issue in transfer learning is how to find a good representation for instances of different …

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

Over the last years, deep convolutional neural networks (ConvNets) have transformed the
field of computer vision thanks to their unparalleled capacity to learn high level semantic …