Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes
Keywords: Autoencoders, Classification
TL;DR: The paper presents a new approach using ratios of autoencoder reconstruction errors to analyze dataset difficulty and identify mislabeled samples
Abstract: We introduce a new framework for analyzing classification datasets based on the ratios of reconstruction errors between autoencoders trained on individual classes. This analysis framework enables efficient characterization of datasets on the sample, class, and entire dataset levels. We define reconstruction error ratios (RERs) that probe classification difficulty and allow its decomposition into (1) finite sample size and (2) Bayes error and decision-boundary complexity. Through systematic study across 19 popular visual datasets, we find that our RER-based dataset difficulty probe strongly correlates with error rate for state-of-the-art (SOTA) classification models. By interpreting sample-level classification difficulty as a label mistakenness score, we further find that RERs achieve SOTA performance on mislabel detection tasks on hard datasets under symmetric and asymmetric label noise.
Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
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Submission Number: 12929
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