Pretrained Foundation Models (PFMs) are regarded as the foundation for various
downstream tasks with different data modalities. A PFM (eg, BERT, ChatGPT, and 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 …

This paper demonstrates an approach for learning highly semantic image representations
without relying on hand-crafted data-augmentations. We introduce the Image-based Joint …

Abstract Machine-learning models for medical tasks can match or surpass the performance
of clinical experts. However, in settings differing from those of the training dataset, the …

Machine learning systems generally assume that the training and testing distributions are
the same. To this end, a key requirement is to develop models that can generalize to unseen …

Most recent self-supervised methods for learning image representations focus on either
producing a global feature with invariance properties, or producing a set of local features …

Self-supervised representation learning has shown remarkable success in a number of
domains. A common practice is to perform data augmentation via hand-crafted …

Recent works in self-supervised learning have advanced the state-of-the-art by relying on
the contrastive learning paradigm, which learns representations by pushing positive pairs, or …

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 …

Semantic communication is viewed as a revolutionary paradigm that can potentially
transform how we design and operate wireless communication systems. However, despite a …