5 accepted papers
The core challenge of high-dimensional and expensive black-box optimization (BBO) is how to obtain better performance faster with little function evaluation cost. The essence of the problem is how to design an efficient optimization strategy tailored to the target task. This paper designs a powerful…
Zero-shot optimization aims to achieve both generalization and performance gains on solving previously unseen black-box optimization problems over SOTA methods without task-specific tuning. Pre-trained optimization models (POMs) address this challenge by learning a general mapping from task features…
Zero-shot optimization involves optimizing a target task that was not seen during training, aiming to provide the optimal solution without or with minimal adjustments to the optimizer. It is crucial to ensure reliable and robust performance in various applications. Current optimizers often struggle…
As a special kind of signal processing technology, image processing has been developed rapidly after the appearance of convolutional neural network (CNN). At present, the semantic segmentation methods are all based on CNN and ignore the advantages of traditional image processing technology. We combi…
It is easy to obtain multi-tasking models from open source platforms or various organizations. However, using these models at the same time will bring a great burden on storage and reduce computing efficiency. In this paper, we propose a transformation-based multi-task fusion method, called transfor…