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2019-12-23 17:06:14 +11:00
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README.md
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This project contains the following neural architecture search algorithms, implemented in [PyTorch](http://pytorch.org).
More NAS resources can be found in [Awesome-NAS](https://github.com/D-X-Y/Awesome-NAS).
- NAS-Bench-102: Extending the Scope of Reproducible Neural Architecture Search, ICLR 2020
- Network Pruning via Transformable Architecture Search, NeurIPS 2019
- One-Shot Neural Architecture Search via Self-Evaluated Template Network, ICCV 2019
- Searching for A Robust Neural Architecture in Four GPU Hours, CVPR 2019
- NAS-Bench-102: Extending the Scope of Reproducible Neural Architecture Search, ICLR 2020
- 10 NAS algorithms for the neural topology in `exps/algos` (see [NAS-Bench-102.md](https://github.com/D-X-Y/NAS-Projects/blob/master/NAS-Bench-102.md) for more details)
- Several typical classification models, e.g., ResNet and DenseNet (see [BASELINE.md](https://github.com/D-X-Y/NAS-Projects/blob/master/BASELINE.md))
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2. Different NAS-searched architectures are defined [here](https://github.com/D-X-Y/NAS-Projects/blob/master/lib/nas_infer_model/DXYs/genotypes.py).
## NAS-Bench-102: Extending the Scope of Reproducible Neural Architecture Search
## [NAS-Bench-102: Extending the Scope of Reproducible Neural Architecture Search](https://openreview.net/forum?id=HJxyZkBKDr)
We build a new benchmark for neural architecture search, please see more details in [NAS-Bench-102.md](https://github.com/D-X-Y/NAS-Projects/blob/master/NAS-Bench-102.md).
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We proposed a Gradient-based searching algorithm using Differentiable Architecture Sampling (GDAS). GDAS is baseed on DARTS and improves it with Gumbel-softmax sampling.
Experiments on CIFAR-10, CIFAR-100, ImageNet, PTB, and WT2 are reported.
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).
### Usage