update README
This commit is contained in:
		| @@ -17,7 +17,6 @@ Some methods use knowledge distillation (KD), which require pre-trained models. | ||||
|  | ||||
| ## [Network Pruning via Transformable Architecture Search](https://arxiv.org/abs/1905.09717) | ||||
|  | ||||
|  | ||||
| <img src="https://d-x-y.github.com/resources/paper-icon/NIPS-2019-TAS.png" width="700"> | ||||
|  | ||||
| Use `bash ./scripts/prepare.sh` to prepare data splits for `CIFAR-10`, `CIFARR-100`, and `ILSVRC2012`. | ||||
| @@ -43,6 +42,7 @@ args: `cifar10` indicates the dataset name, `ResNet56` indicates the basemodel n | ||||
|  | ||||
| ## One-Shot Neural Architecture Search via Self-Evaluated Template Network | ||||
|  | ||||
| <img src="https://d-x-y.github.com/resources/paper-icon/ICCV-2019-SETN.png" width="550"> | ||||
| Train the searched SETN-searched CNN on CIFAR-10, CIFAR-100, and ImageNet. | ||||
| ``` | ||||
| CUDA_VISIBLE_DEVICES=0 bash ./scripts/nas-infer-train.sh cifar10  SETN 96 -1 | ||||
| @@ -55,6 +55,8 @@ Searching codes come soon! | ||||
|  | ||||
| ## [Searching for A Robust Neural Architecture in Four GPU Hours](http://openaccess.thecvf.com/content_CVPR_2019/papers/Dong_Searching_for_a_Robust_Neural_Architecture_in_Four_GPU_Hours_CVPR_2019_paper.pdf) | ||||
|  | ||||
| <img src="https://d-x-y.github.com/resources/paper-icon/CVPR-2019-GDAS.png" width="450"> | ||||
|  | ||||
| The old version is located at [`others/GDAS`](https://github.com/D-X-Y/NAS-Projects/tree/master/others/GDAS) and a paddlepaddle implementation is locate at [`others/paddlepaddle`](https://github.com/D-X-Y/NAS-Projects/tree/master/others/paddlepaddle). | ||||
|  | ||||
| Train the searched GDAS-searched CNN on CIFAR-10, CIFAR-100, and ImageNet. | ||||
|   | ||||
		Reference in New Issue
	
	Block a user