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New study compares mini-batch sampling for long-tailed image classification

By PulseAugur Editorial · [1 sources] ·

A new study on arXiv investigates mini-batch sampling strategies for long-tailed image classification tasks, focusing on the CIFAR-100-LT dataset. Researchers compared uniform instance sampling, class-balanced sampling, square-root sampling, and progressively balanced sampling using ResNet-32. The findings suggest that progressive sampling significantly improves accuracy for tail classes, showing a 25% relative gain over the uniform baseline at a high imbalance ratio, without compromising overall accuracy. AI

IMPACT Provides insights into optimizing training for datasets with imbalanced class distributions, crucial for real-world AI applications.

RANK_REASON Academic paper on machine learning methodology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New study compares mini-batch sampling for long-tailed image classification

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Academic paper on machine learning methodology. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Siyu Yuan ·

    Mini-batch Sampling Strategies for Long-Tailed Image Classification: An Empirical Study on CIFAR-100-LT

    arXiv:2609.16365v1 Announce Type: new Abstract: Real-world datasets often exhibit long-tailed class distributions, where a few head classes contain a large number of training samples while a large number of tail classes have only a few. The composition of each mini-batch, determi…

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