Despite being responsible for state-of-the-art results in several computer vision and natural
language processing tasks, neural networks have faced harsh criticism due to some of their …

We aim at advancing blind image quality assessment (BIQA), which predicts the human
perception of image quality without any reference information. We develop a general and …

Recommender systems are an essential tool to relieve the information overload challenge
and play an important role in people's daily lives. Since recommendations involve …

The goal of multi-task learning is to enable more efficient learning than single task learning
by sharing model structures for a diverse set of tasks. A standard multi-task learning …

Multi-task learning can leverage information learned by one task to benefit the training of
other tasks. Despite this capacity, naively training all tasks together in one model often …

Multi-task learning (MTL) is a subfield of machine learning in which multiple tasks are
simultaneously learned by a shared model. Such approaches offer advantages like …

With the advent of deep learning, many dense prediction tasks, ie, tasks that produce pixel-
level predictions, have seen significant performance improvements. The typical approach is …

Multi-Task Learning has emerged as a methodology in which multiple tasks are jointly
learned by a shared learning algorithm, such as a DNN. MTL is based on the assumption …

Transfer learning (TL) utilizes data or knowledge from one or more source domains to
facilitate learning in a target domain. It is particularly useful when the target domain has very …

Linear scalarization, ie, combining all loss functions by a weighted sum, has been the
default choice in the literature of multi-task learning (MTL) since its inception. In recent years …