Learning to leverage the relationship among diverse image restoration tasks is quite
beneficial for unraveling the intrinsic ingredients behind the degradation. Recent years have …

Multi-Task Learning (MTL) has achieved success in various fields. However, how to balance
different tasks to achieve good performance is a key problem. To achieve the task balancing …

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 …

Multi-task dense scene understanding, which learns a model for multiple dense prediction
tasks, has a wide range of application scenarios. Modeling long-range dependency and …

Multi-objective learning (MOL) often arises in emerging machine learning problems when
multiple learning criteria or tasks need to be addressed. Recent works have developed …

Prompt tuning for pre-trained masked language models (MLM) has shown promising
performance in natural language processing tasks with few labeled examples. It tunes a …

Multi-objective optimization (MOO) has become an influential framework in many machine
learning problems with multiple objectives such as learning with multiple criteria and multi …

Machine learning problems with multiple objectives appear either i) in learning with multiple
criteria where learning has to make a trade-off between multiple performance metrics such …

In multi-task learning (MTL), gradient balancing has recently attracted more research interest
than loss balancing since it often leads to better performance. However, loss balancing is …

Meta-learning has attracted attention due to its strong ability to learn experiences from
known tasks, which can speed up and enhance the learning process for new tasks …