AutoMix: Unveiling the Power of Mixup for Stronger Classifiers

(ECCV 2022 Oral)

Zicheng Liu^*,1,2, Siyuan Li^*,1,2, Di Wu^1,2, Zhiyuan Chen¹, Lirong Wu^1,2, Stan Z. Li^†,1

¹Westlake University, ²Zhejiang University

We propose a novel automatic mixup (AutoMix) framework, where the mixup policy is parameterized and serves the ultimate classification goal directly. Specifically, AutoMix reformulates the mixup classification into two sub-tasks (i.e., mixed sample generation and mixup classification) with corresponding sub-networks and solves them in a bi-level optimization framework. For the generation, a learnable lightweight mixup generator, Mix Block, is designed to generate mixed samples by modeling patch-wise relationships under the direct supervision of the corresponding mixed labels. To prevent the degradation and instability of bi-level optimization, we further introduce a momentum pipeline to train AutoMix in an end-to-end manner. Extensive experiments on nine image benchmarks prove the superiority of AutoMix compared with state-of-the-arts in various classification scenarios and downstream tasks.

Catalog

We plan to update this timm implementation of AutoMix in a few months. Please watch us for the latest release or use our OpenMixup implementations.

• Image Classification Code with OpenMixup [code]
• CIFAR-10/100 and Tiny-ImageNet Training and Validation Code with timm [code]
• ImageNet-1K Training and Validation Code [code]
• Image Classification on Google Colab and Notebook Demo

Installation

Please check INSTALL.md for installation instructions.

Small-scale Image Classification

Please refer to OpenMixup implementations of CIFAR-100 and Tiny-ImageNet.

ImageNet Classification

1. Training and Validation

See TRAINING.md for ImageNet-1K training and validation instructions, or refer to our OpenMixup implementations. We released pre-trained models on OpenMixup.

2. ImageNet-1K Trained Models

Please refer to mixup_benchmarks in OpenMixup implementations for results and models.

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License

This project is released under the Apache 2.0 license.

Acknowledgement

Our implementation is mainly based on the following codebases. We gratefully thank the authors for their wonderful works.

• pytorch-image-models: PyTorch image models, scripts, pretrained weights.
• OpenMixup: CAIRI Supervised, Semi- and Self-Supervised Visual Representation Learning Toolbox and Benchmark.

Citation

If you find this repository helpful, please consider citing:

@InProceedings{liu2022automix,
      title={AutoMix: Unveiling the Power of Mixup for Stronger Classifiers},
      author={Zicheng Liu and Siyuan Li and Di Wu and Zhiyuan Chen and Lirong Wu and Jianzhu Guo and Stan Z. Li},
      booktitle={European Conference on Computer Vision},
      pages={441--458},
      year={2022},
}

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