  The problem of long-tailed recognition (LTR) has received attention in recent
years due to the fundamental power-law distribution of objects in the
real-world. Most recent works in LTR use softmax classifiers that are biased in
that they correlate classifier norm with the amount of training data for a
given class. In this work, we show that learning prototype classifiers
addresses the biased softmax problem in LTR. Prototype classifiers can deliver
promising results simply using Nearest-Class- Mean (NCM), a special case where
prototypes are empirical centroids. We go one step further and propose to
jointly learn prototypes by using distances to prototypes in representation
space as the logit scores for classification. Further, we theoretically analyze
the properties of Euclidean distance based prototype classifiers that lead to
stable gradient-based optimization which is robust to outliers. To enable
independent distance scales along each channel, we enhance Prototype
classifiers by learning channel-dependent temperature parameters. Our analysis
shows that prototypes learned by Prototype classifiers are better separated
than empirical centroids. Results on four LTR benchmarks show that Prototype
classifier outperforms or is comparable to state-of-the-art methods. Our code
is made available at
https://github.com/saurabhsharma1993/prototype-classifier-ltr.
