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Learning to classify images incrementally from imbalanced data

Published on:

2 November 2023

Primary Category:

Computer Vision and Pattern Recognition

Paper Authors:

Jayateja Kalla,

Soma Biswas

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Key Details

Proposes a two-stage framework for long-tail class incremental learning

Learns robust features in stage 1 using mixup regularization

Aligns classifiers in stage 2 based on global variance measure

Avoids need for balanced data or added model layers

Shows consistent gains over prior state-of-the-art on CIFAR and ImageNet

AI generated summary

Learning to classify images incrementally from imbalanced data

This paper introduces a two-stage approach to enable image classification models to continuously learn new classes from imbalanced long-tail distributions of data. The approach mitigates catastrophic forgetting and bias towards majority classes by learning robust features in stage 1, then aligning classifiers using global variance and prototypes in stage 2.

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