mlsanityData to 14 Oct 2026

ChimeraMix: Image Classification on Small Datasets via Masked Feature Mixing

arXiv 2202.11616 ↗First posted 23 Feb 2022cs.CVcited by 4

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Abstract (arXiv)

Deep convolutional neural networks require large amounts of labeled data samples. For many real-world applications, this is a major limitation which is commonly treated by augmentation methods. In this work, we address the problem of learning deep neural networks on small datasets. Our proposed architecture called ChimeraMix learns a data augmentation by generating compositions of instances. The generative model encodes images in pairs, combines the features guided by a mask, and creates new samples. For evaluation, all methods are trained from scratch without any additional data. Several experiments on benchmark datasets, e.g. ciFAIR-10, STL-10, and ciFAIR-100, demonstrate the superior performance of ChimeraMix compared to current state-of-the-art methods for classification on small datasets.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 27 of its 54 references that are in the map. First posted on arXiv 23 Feb 2022.

Builds on

27 of 54 references.

References · 27

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15 more
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Citing papers per year: 2023: 3; 2024: 1.

4 papers.

  1. 2024RAW-Diffusion: RGB-Guided Diffusion Models for High-Fidelity RAW Image Generationcited by 8
  2. 2023Take 5: Interpretable Image Classification with a Handful of Featurescited by 7
  3. 2023Compensation Learning in Semantic Segmentationcited by 5
  4. 2023No Data Augmentation? Alternative Regularizations for Effective Training on Small Datasetscited by 1