Seminar by Jingwei Zhang

Addressing label noise in imbalance dataset learning

Start: 5/04/2024, 10:00 - 11:00
Location: B00.35

Abstract: Achieving reliable performance in deep learning usually requires the ability of learning against label noise. This challenge is generally addressed by identifying incorrectly labelled instances through the memorization effect, wherein networks prioritize learning patterns from correctly labelled instances before memorizing incorrectly labelled data. However, these methods often encounter difficulties when faced with imbalanced datasets, a common scenario in real-world applications. This presentation dives into the reasons why existing methods may struggle with imbalanced datasets, attributing discrepancies in loss distribution among classes. Subsequently, the presentation provides some insights into strategies for addressing label noise in imbalanced dataset learning.

Organized by: Jingwei Zhang