34 lines
1.2 KiB
Markdown
34 lines
1.2 KiB
Markdown
# Sample-Wise Activation Patterns for Ultra-Fast NAS <br/> (ICLR 2024 Spotlight)
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SWAP-Score, based on sample-wise activation patterns, is a metric that assesses the performance of neural networks without training.
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# Usage
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The following instruction demonstrates the usage of evaluating network's performance through SWAP-Score.
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**/src/metrics/swap.py** contains the core components of SWAP-Score.
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**/datasets/DARTS_archs_CIFAR10.csv** contains 1000 architectures (randomly sampled from DARTS search space) along with their CIFAR-10 validation accuracies (trained for 200 epochs).
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* Install necessary dependencies (a new virtual environment is suggested).
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```
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cd SWAP
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pip install -r requirements.txt
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```
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* Calculate the correlation between SWAP-Score and CIFAR-10 validation accuracies of 1000 CNN architectures.
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```
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python correlation.py
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```
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If you use or build on our code, please consider citing our paper:
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```
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@inproceedings{
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peng2024swapnas,
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title={{SWAP}-{NAS}: Sample-Wise Activation Patterns for Ultra-fast {NAS}},
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author={Yameng Peng and Andy Song and Haytham M. Fayek and Vic Ciesielski and Xiaojun Chang},
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booktitle={The Twelfth International Conference on Learning Representations},
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year={2024},
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url={https://openreview.net/forum?id=tveiUXU2aa}
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}
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```
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