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@@ -17,8 +17,12 @@ Some methods use knowledge distillation (KD), which require pre-trained models.
## [Network Pruning via Transformable Architecture Search](https://arxiv.org/abs/1905.09717) ## [Network Pruning via Transformable Architecture Search](https://arxiv.org/abs/1905.09717)
In this paper, we proposed a differentiable searching strategy for transformable architectures, i.e., searching for the depth and width of a deep neural network. In this paper, we proposed a differentiable searching strategy for transformable architectures, i.e., searching for the depth and width of a deep neural network.
You could see the highlight of our Transformable Architecture Search (TAS) at our [project page](https://xuanyidong.com/assets/projects/NeurIPS-2019-TAS.html).
<img src="https://d-x-y.github.com/resources/paper-icon/NIPS-2019-TAS.png" width="700"> <p float="left">
<img src="https://d-x-y.github.com/resources/paper-icon/NIPS-2019-TAS.png" width="680px"/>
<img src="https://d-x-y.github.com/resources/videos/NeurIPS-2019-TAS/TAS-arch.gif?raw=true" width="180px"/>
</p>
### Usage ### Usage
@@ -46,9 +50,10 @@ args: `cifar10` indicates the dataset name, `ResNet56` indicates the basemodel n
## One-Shot Neural Architecture Search via Self-Evaluated Template Network ## One-Shot Neural Architecture Search via Self-Evaluated Template Network
<img align="right" src="https://d-x-y.github.com/resources/paper-icon/ICCV-2019-SETN.png" width="450">
Highlight: we equip one-shot NAS with an architecture sampler and train network weights using uniformly sampling. Highlight: we equip one-shot NAS with an architecture sampler and train network weights using uniformly sampling.
<img src="https://d-x-y.github.com/resources/paper-icon/ICCV-2019-SETN.png" width="450">
### Usage ### Usage
@@ -64,9 +69,10 @@ 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) ## [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)
We proposed a gradient-based searching algorithm using differentiable architecture sampling (improving DARTS with Gumbel-softmax sampling).
<img src="https://d-x-y.github.com/resources/paper-icon/CVPR-2019-GDAS.png" width="300"> <img align="right" src="https://d-x-y.github.com/resources/paper-icon/CVPR-2019-GDAS.png" width="300">
We proposed a gradient-based searching algorithm using differentiable architecture sampling (improving DARTS with Gumbel-softmax sampling).
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). 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).