ChimeraMix: Image Classification on Small Datasets via Masked Feature Mixing
Abstract
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. Code is available at https://github.com/creinders/ChimeraMix.
Cite
Text
Reinders et al. "ChimeraMix: Image Classification on Small Datasets via Masked Feature Mixing." International Joint Conference on Artificial Intelligence, 2022. doi:10.24963/IJCAI.2022/181Markdown
[Reinders et al. "ChimeraMix: Image Classification on Small Datasets via Masked Feature Mixing." International Joint Conference on Artificial Intelligence, 2022.](https://mlanthology.org/ijcai/2022/reinders2022ijcai-chimeramix/) doi:10.24963/IJCAI.2022/181BibTeX
@inproceedings{reinders2022ijcai-chimeramix,
title = {{ChimeraMix: Image Classification on Small Datasets via Masked Feature Mixing}},
author = {Reinders, Christoph and Schubert, Frederik and Rosenhahn, Bodo},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2022},
pages = {1298-1305},
doi = {10.24963/IJCAI.2022/181},
url = {https://mlanthology.org/ijcai/2022/reinders2022ijcai-chimeramix/}
}