Abstract Machine learning methods strive to acquire a robust model during the training
process that can effectively generalize to test samples, even in the presence of distribution …

Over the past decade, domain adaptation has become a widely studied branch of transfer
learning which aims to improve performance on target domains by leveraging knowledge …

Recent image generation models such as Stable Diffusion have exhibited an impressive
ability to generate fairly realistic images starting from a simple text prompt. Could such …

Federated Learning (FL) is an emerging distributed learning paradigm under privacy
constraint. Data heterogeneity is one of the main challenges in FL, which results in slow …

Abstract Knowledge distillation aims to compress a powerful yet cumbersome teacher model
into a lightweight student model without much sacrifice of performance. For this purpose …

This chapter provides approaches to the problem of quantizing the numerical values in deep
Neural Network computations, covering the advantages/disadvantages of current methods …

Training deep neural networks requires gradient estimation from data batches to update
parameters. Gradients per parameter are averaged over a set of data and this has been …

In recent years, deep neural networks have been successful in both industry and academia,
especially for computer vision tasks. The great success of deep learning is mainly due to its …

Abstract Unsupervised Domain Adaptation (UDA) can tackle the challenge that
convolutional neural network (CNN)-based approaches for semantic segmentation heavily …

Deep neural models, in recent years, have been successful in almost every field, even
solving the most complex problem statements. However, these models are huge in size with …