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 …

The booming development of deep learning applications and services heavily relies on
large deep learning models and massive data in the cloud. However, cloud-based deep …

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 …

Federated Learning (FL) is a machine learning setting where many devices collaboratively
train a machine learning model while kee** the training data decentralized. In most of the …

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 …

Recently, person re-identification (Re-ID) has achieved great progress. However, current
methods largely depend on color appearance, which is not reliable when a person changes …

There is a strong incentive to develop versatile learning techniques that can transfer the
knowledge of class-separability from a labeled source domain to an unlabeled target …

Most works on person re-identification (ReID) take advantage of large backbone networks
such as ResNet, which are designed for image classification instead of ReID, for feature …

Deep model fusion/merging is an emerging technique that merges the parameters or
predictions of multiple deep learning models into a single one. It combines the abilities of …