Feature Generation for Long-tail Classification

• URL: http://arxiv.org/abs/2111.05956v1
• Date: Wed, 10 Nov 2021 21:34:29 GMT
• Title: Feature Generation for Long-tail Classification
• Authors: Rahul Vigneswaran and Marc T. Law and Vineeth N. Balasubramanian and Makarand Tapaswi
• Abstract summary: We show how to generate meaningful features by estimating the tail category's distribution. We also present a qualitative analysis of generated features using t-SNE visualizations and analyze the nearest neighbors used to calibrate the tail class distributions.
• Score: 36.186909933006675
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The visual world naturally exhibits an imbalance in the number of object or scene instances resulting in a \emph{long-tailed distribution}. This imbalance poses significant challenges for classification models based on deep learning. Oversampling instances of the tail classes attempts to solve this imbalance. However, the limited visual diversity results in a network with poor representation ability. A simple counter to this is decoupling the representation and classifier networks and using oversampling only to train the classifier. In this paper, instead of repeatedly re-sampling the same image (and thereby features), we explore a direction that attempts to generate meaningful features by estimating the tail category's distribution. Inspired by ideas from recent work on few-shot learning, we create calibrated distributions to sample additional features that are subsequently used to train the classifier. Through several experiments on the CIFAR-100-LT (long-tail) dataset with varying imbalance factors and on mini-ImageNet-LT (long-tail), we show the efficacy of our approach and establish a new state-of-the-art. We also present a qualitative analysis of generated features using t-SNE visualizations and analyze the nearest neighbors used to calibrate the tail class distributions. Our code is available at https://github.com/rahulvigneswaran/TailCalibX.

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