報告題目：Handling Class Imbalance and Small Sample Issues: Foundation, Algorithms, 

                    and Applications

報告時間：2024年11月1日15:00-16:30

報告地點：437bwin必贏國際官網(wǎng)B405

報告人：王子棟

報告人單位：英國倫敦Brunel大學(xué)

報告人簡介：王子棟，現(xiàn)任英國倫敦Brunel University講席教授，歐洲科學(xué)院院士，歐洲科學(xué)與藝術(shù)院院士，IEEE Fellow，International Journal of Systems Science主編，Neurocomputing主編。多年來從事控制理論、機器學(xué)習(xí)、生物信息學(xué)等方面研究，在SCI刊物上發(fā)表國際論文七百余篇。現(xiàn)任或曾任十二種國際刊物的主編、副編輯或編委。曾任旅英華人自動化及計算機協(xié)會主席、東華大學(xué)國家級領(lǐng)軍人才、清華大學(xué)國家級專家。

報告摘要：In big data analysis, it is usually difficult to collect high-quality labels, and this leads to two issues in deep learning, namely, the class imbalance issue and the small sample issue. In this talk, we first introduce some background knowledge about the deep learning from the perspectives of concepts, techniques, applications and challenges. Then, we introduce three state-of-the-art algorithms for solving the class imbalance and small sample issues: 1) a novel contrastive adversarial network for minor-class data augmentation; 2) a novel subdomain-alignment data augmentation approach; and 3) a novel prototype-assisted contrastive adversarial network for weak-shot learning. All the three algorithms are applied to pipeline fault diagnosis, which outperform existing ones. Finally, we conclude our main contributions and some future directions.

邀請人：杜博