A PyTorch Implementation of i-MAE: Linearly Separable Representation in MAE
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We provide a PyTorch/GPU based implementation of our technical report i-MAE: Are Latent Representations in Masked Autoencoders Linearly Separable?
Catalog
- Pretrain demo with Colab
- More code coming soon!
Visualization demo
Please visit our interactive demo on our website, or run our visualization demo with a Colab notebook
Acknowledgement
This repository is based on timm and MAE repositories.
License
This project is under the CC-BY-NC 4.0 license. See LICENSE for details.
Citation
If you find this repository helpful, please consider citing our work:
@article{zhang2022i-mae,
title={i-MAE: Are Latent Representations in Masked Autoencoders Linearly Separable?},
author = {Zhang, Kevin and Shen, Zhiqiang},
journal={arXiv preprint arXiv:2210.11470},
year={2022}
}
