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On Representation Learning Under Class Imbalance
Ravid Shwartz-Ziv · Micah Goldblum · Yucen (Lily) Li · C. Bayan Bruss · Andrew Gordon Wilson

Unlike carefully curated academic benchmarks, real-world datasets are often highly class-imbalanced, especially in safety-critical scenarios. Through extensive empirical investigation, we study a number of foundational learning behaviors for various models such as neural networks, gradient-boosted decision trees, and SVMs under class imbalance across a range of domains. Motivated by our observation that re-balancing class-imbalanced training data is ineffective, we show that several simple techniques for improving representation learning are effective in this setting: (1) self-supervised pre-training is insensitive to imbalance and can be used for feature learning before fine-tuning on labels; (2) Bayesian inference is effective because neural networks are especially underspecified under class imbalance; (3) flatness-seeking regularization pulls decision boundaries away from minority samples, especially when we seek minima that are particularly flat on the minority samples’ loss.

Author Information

Ravid Shwartz-Ziv (Hebrew University of Jerusalem)
Micah Goldblum (University of Maryland)
Yucen (Lily) Li (New York University)
C. Bayan Bruss (Capital One)
Andrew Gordon Wilson (New York University)

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