Abstract Pretrained Foundation Models (PFMs) are regarded as the foundation for various
downstream tasks across different data modalities. A PFM (eg, BERT, ChatGPT, GPT-4) is …

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 …

The recent breakthroughs in natural language processing for model pretraining on large
quantities of data have opened the way for similar foundation models in computer vision …

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 (SSL) is rapidly closing the gap with supervised methods on large
computer vision benchmarks. A successful approach to SSL is to learn embeddings which …

Self-supervised learning has gained popularity because of its ability to avoid the cost of
annotating large-scale datasets. It is capable of adopting self-defined pseudolabels as …

Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

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

Contrastive representation learning has been outstandingly successful in practice. In this
work, we identify two key properties related to the contrastive loss:(1) alignment (closeness) …

Vertical Federated Learning (VFL) is a federated learning setting where multiple parties with
different features about the same set of users jointly train machine learning models without …