Memory-based Jitter: Improving Visual Recognition on Long-tailed Data with Diversity In Memory

Understand

This paper considers deep visual recognition on long-tailed data.

• To be general, we consider two applied scenarios, \ie, deep classification and deep metric learning.
• Under the long-tailed data distribution, the majority classes (\ie, tail classes) only occupy relatively few samples and are prone to lack of within-class diversity.
• A radical solution is to augment the tail classes with higher diversity.