The renaissance of deep learning has provided promising solutions to various tasks. While
conventional deep learning models are constructed for a single specific task, multi-task deep …

Addressing issues related to multi-label classification is relevant in many fields of
applications. In this work. We present a multi-label classification architecture based on Multi …

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 …

Multi-task dense scene understanding is a thriving research domain that requires
simultaneous perception and reasoning on a series of correlated tasks with pixel-wise …

In this paper, we take a new look at real-world image super-resolution (real-SR) from a multi-
task learning perspective. We demonstrate that the conventional formulation of real-SR can …

Learning discriminative task-specific features simultaneously for multiple distinct tasks is a
fundamental problem in multi-task learning. Recent state-of-the-art models consider directly …

In this paper, we explore the advantages of utilizing transformer structures for addressing
multi-task learning (MTL). Specifically, we demonstrate that models with transformer …

Many recently proposed methods for Neural Architecture Search (NAS) can be formulated
as bilevel optimization. For efficient implementation, its solution requires approximations of …

Understanding the structure of multiple related tasks allows for multi-task learning to improve
the generalisation ability of one or all of them. However, it usually requires training each …

Neural architecture search (NAS) has attracted increasing attention. In recent years,
individual search methods have been replaced by weight-sharing search methods for higher …