Deep supervised learning algorithms typically require a large volume of labeled data to
achieve satisfactory performance. However, the process of collecting and labeling such data …

Anomaly detection, aka outlier detection or novelty detection, has been a lasting yet active
research area in various research communities for several decades. There are still some …

We present a novel masked image modeling (MIM) approach, context autoencoder (CAE),
for self-supervised representation pretraining. We pretrain an encoder by making predictions …

In this paper, we question if self-supervised learning provides new properties to Vision
Transformer (ViT) that stand out compared to convolutional networks (convnets). Beyond the …

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 …

Recent self-supervised methods for image representation learning are based on maximizing
the agreement between embedding vectors from different views of the same image. A trivial …

In this paper, we propose an online clustering method called Contrastive Clustering (CC)
which explicitly performs the instance-and cluster-level contrastive learning. To be specific …

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

Deep supervised learning has achieved great success in the last decade. However, its
defects of heavy dependence on manual labels and vulnerability to attacks have driven …

This paper presents Prototypical Contrastive Learning (PCL), an unsupervised
representation learning method that addresses the fundamental limitations of instance-wise …