Feature Space Augmentation for Long-Tailed Data

2020/08/09 by Peng Chu, Xiao Bian, Chu, Peng +5 · 6 citations
Computer Science · Medicine · #COVID-19 diagnosis using AI #Computer Vision and Pattern Recognition (cs.CV) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Imbalanced Data Classification Techniques

paper · pdf · doi:10.48550/arxiv.2008.03673

openalex publication_date 2020/08/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Real-world data often follow a long-tailed distribution as the frequency of each class is typically different. For example, a dataset can have a large number of under-represented classes and a few classes with more than sufficient data. However, a model to represent the dataset is usually expected to have reasonably homogeneous performances across classes. Introducing class-balanced loss and advanced methods on data re-sampling and augmentation are among the best practices to alleviate the data imbalance problem. However, the other part of the problem about the under-represented classes will have to rely on additional knowledge to recover the missing information. In this work, we present a novel approach to address the long-tailed problem by augmenting the under-represented classes in the feature space with the features learned from the classes with ample samples. In particular, we decompose the features of each class into a class-generic component and a class-specific component using class activation maps. Novel samples of under-represented classes are then generated on the fly during training stages by fusing the class-specific features from the under-represented classes with the class-generic features from confusing classes. Our results on different datasets such as iNaturalist, ImageNet-LT, Places-LT and a long-tailed version of CIFAR have shown the state of the art performances.

Citations

• The Pascal Visual Object Classes (VOC) Challenge
• ImageNet Large Scale Visual Recognition Challenge
• ImageNet classification with deep convolutional neural networks
• A systematic study of the class imbalance problem in convolutional neural networks
• Learning Multiple Layers of Features from Tiny Images

Cited by

• Balanced Learning for Domain Adaptive Semantic Segmentation
• Space Alignment Matters: The Missing Piece for Inducing Neural Collapse in Long-Tailed Learning
• Rectifying Soft-Label Entangled Bias in Long-Tailed Dataset Distillation
• Learning by Imagining: Debiased Feature Augmentation for Compositional Zero-Shot Learning
• Augment to Segment: Tackling Pixel-Level Imbalance in Wheat Disease and Pest Segmentation
• Calibrating Biased Distribution in VFM-derived Latent Space via Cross-Domain Geometric Consistency

Related

• CUDA: Curriculum of Data Augmentation for Long-Tailed Recognition
• Long-Tailed Classification with Gradual Balanced Loss and Adaptive Feature Generation
• Feature Generation for Long-tail Classification
• Deep Representation Learning on Long-tailed Data: A Learnable Embedding Augmentation Perspective
• Learning from Neighbors: Category Extrapolation for Long-Tail Learning