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Noisy Differentiable Architecture Search [BMVC 2021]

This repository includes the implementation of NoisyDARTS.

Requirements

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.

Searching

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 &

Training

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 &

Evaluation

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.

Pre-trained Models

You can download pretrained models here:

Results

NoisyDARTS Models Searched on CIFAR-10

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 :

Image Classification on ImageNet

Model name Top 1 Accuracy Top 5 Accuracy
NoisyDARTS-A 77.9% 94.0%

Citation

@inproceedings{chu2021noisy,
  title={Noisy Differentiable Architecture Search},
  author={Chu, Xiangxiang and Zhang, Bo},
  booktitle={BMVC},
  year={2021}
}

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