Representation learning with small labeled data have emerged in many problems, since the
success of deep neural networks often relies on the availability of a huge amount of labeled …

Abstract Multi-scale Vision Transformer (ViT) has emerged as a powerful backbone for
computer vision tasks, while the self-attention computation in Transformer scales …

This paper studies the unsupervised embedding learning problem, which requires an
effective similarity measurement between samples in low-dimensional embedding space …

We present a novel clustering objective that learns a neural network classifier from scratch,
given only unlabelled data samples. The model discovers clusters that accurately match …

Deep Neural Networks (DNNs) excel on many complex perceptual tasks but it has proven
notoriously difficult to understand how they reach their decisions. We here introduce a high …

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 …

Supervised operator learning is an emerging machine learning paradigm with applications
to modeling the evolution of spatio-temporal dynamical systems and approximating general …

Recent style transfer models have provided promising artistic results. However, given a
photograph as a reference style, existing methods are limited by spatial distortions or …

Image classification with small datasets has been an active research area in the recent past.
However, as research in this scope is still in its infancy, two key ingredients are missing for …

Although recent studies empirically show that injecting Convolutional Neural Networks
(CNNs) into Vision Transformers (ViTs) can improve the performance of person re …