Research map / Label-efficient and robust learning / Recognition and long-tail
Geometry of Long-Tailed Representation Learning: Rebalancing Features for Skewed Distributions
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Studies the geometry of feature representations under long-tailed training and proposes a method to rebalance skewed features for long-tailed recognition.
Problem
Long-tailed data skew representations so head classes dominate the feature space; the underlying cause and extent of this skew are not clearly understood.
Approach
Analyzes the geometry of learned representations under long-tailed distributions to explain feature skew, then rebalances features to counter head-class dominance. Details beyond the abstract are not available.
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