This repository includes the implementation of NoisyDARTS.
To install requirements:
pip install -r requirements.txt
We use CIFAR-10 and ImageNet datasets, which can be downloaded by torchvision automatically. We follow the standard preprocessing for both datasets.
To perform a standard NoisyDARTS search, execute for example:
nohup python -u train_search.py --exp_name noisy_darts_a --factor_skip 0.2 --add_noise_skip --gpu 0 --seed 10 > noisy_darts_a.log 2>&1 &
To train the model(s) in the paper, run for example:
nohup python -u train.py --auxiliary --cutout --save noisy_darts_a --arch noisy_darts_a --gpu 0 > noisy_darts_a.log 2>&1 &
To evaluate CIFAR models, run for example:
python verify.py --auxiliary --arch noisy_darts_a --model-path pretrained/noisy_darts_a.pt
More evalutaion commands can be found in scripts/run_verify.sh.
You can download pretrained models here:
- All NoisyDARTS models can be downloaded from this link.
| Models | Search Strategy | Multiply-adds (M) | Parameters (M) | Top-1 (%) |
|---|---|---|---|---|
| NoisyDARTS-a | Gaussian, lambda=0.2 | 534 | 3.25 | 97.61 |
| NoisyDARTS-b | Gaussian, lambda=0.1 | 511 | 3.09 | 97.53 |
| NoisyDARTS-c | Uniform, lambda=0.2 | 539 | 3.33 | 97.40 |
| NoisyDARTS-d | Uniform, lambda=0.1 | 501 | 3.06 | 97.42 |
| NoisyDARTS-e | Gaussian, Multiplicative, std=0.1 | 539 | 3.24 | 97.55 |
| NoisyDARTS-f | Gaussian, Multiplicative, std=0.2 | 443 | 2.68 | 97.18 |
| NoisyDARTS-g | Gaussian, lambda=0.2, mean=0.5 | 549 | 3.32 | 97.49 |
| NoisyDARTS-h | Gaussian, lambda=0.2, mean=1.0 | 511 | 3.01 | 97.35 |
| NoisyDARTS-i | Gaussian, lambda=0.1, mean=0.5 | 495 | 3.07 | 97.28 |
| NoisyDARTS-j | Gaussian, lambda=0.1, mean=1.0 | 476 | 2.94 | 97.21 |
Our model achieves the following performance on :
| Model name | Top 1 Accuracy | Top 5 Accuracy |
|---|---|---|
| NoisyDARTS-A | 77.9% | 94.0% |
@inproceedings{chu2021noisy,
title={Noisy Differentiable Architecture Search},
author={Chu, Xiangxiang and Zhang, Bo},
booktitle={BMVC},
year={2021}
}