This paper explores the environmental impact of the super-linear growth trends for AI from a
holistic perspective, spanning Data, Algorithms, and System Hardware. We characterize the …

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

Learning with few labeled data has been a longstanding problem in the computer vision and
machine learning research community. In this paper, we introduced a new semi-supervised …

In this paper we tackle the problem of Generalized Category Discovery (GCD). Specifically,
given a dataset with labelled and unlabelled images, the task is to cluster all images in the …

Pre-training models on large scale datasets, like ImageNet, is a standard practice in
computer vision. This paradigm is especially effective for tasks with small training sets, for …

This work studies the bias issue of pseudo-labeling, a natural phenomenon that widely
occurs but often overlooked by prior research. Pseudo-labels are generated when a …

Out-of-distribution (OOD) detection is a critical task for reliable machine learning. Recent
advances in representation learning give rise to distance-based OOD detection, where …

Deep neural networks achieve remarkable performances on a wide range of tasks with the
aid of large-scale labeled datasets. Yet these datasets are time-consuming and labor …

We study semi-supervised learning (SSL) for vision transformers (ViT), an under-explored
topic despite the wide adoption of the ViT architectures to different tasks. To tackle this …