Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect

Understand

As the class size grows, maintaining a balanced dataset across many classes is challenging because the data are long-tailed in nature; it is even impossible when the sample-of-interest co-exists with each other in one collectable unit, e.g., multiple visual instances in one image.

• Therefore, long-tailed classification is the key to deep learning at scale.
• However, existing methods are mainly based on re-weighting/re-sampling heuristics that lack a fundamental theory.
• In this paper, we establish a causal inference framework, which not only unravels the whys of previous methods, but also derives a new principled solution.