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simplify DARTS codes and update affine/track

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D-X-Y
2020-01-11 18:46:31 +11:00
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README.md
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@@ -15,7 +15,7 @@ More NAS resources can be found in [Awesome-NAS](https://github.com/D-X-Y/Awesom
Please install `PyTorch>=1.2.0`, `Python>=3.6`, and `opencv`.
The CIFAR and ImageNet should be downloaded and extracted into `$TORCH_HOME`.
CIFAR and ImageNet should be downloaded and extracted into `$TORCH_HOME`.
Some methods use knowledge distillation (KD), which require pre-trained models. Please download these models from [Google Driver](https://drive.google.com/open?id=1ANmiYEGX-IQZTfH8w0aSpj-Wypg-0DR-) (or train by yourself) and save into `.latent-data`.
### Usefull tools
@@ -150,6 +150,7 @@ If you find that this project helps your research, please consider citing some o
title = {NAS-Bench-102: Extending the Scope of Reproducible Neural Architecture Search},
author = {Dong, Xuanyi and Yang, Yi},
booktitle = {International Conference on Learning Representations (ICLR)},
url = {https://openreview.net/forum?id=HJxyZkBKDr},
year = {2020}
}
@inproceedings{dong2019tas,