Sample-Wise Activation Patterns for Ultra-Fast NAS
(ICLR 2024 Spotlight)

SWAP-Score, based on sample-wise activation patterns, is a metric that assesses the performance of neural networks without training.

Usage

The following instruction demonstrates the usage of evaluating network's performance through SWAP-Score.

/src/metrics/swap.py contains the core components of SWAP-Score.

/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).

• Install necessary dependencies (a new virtual environment is suggested).

cd SWAP
pip install -r requirements.txt

• Calculate the correlation between SWAP-Score and CIFAR-10 validation accuracies of 1000 CNN architectures.

python correlation.py

If you use or build on our code, please consider citing our paper:

@inproceedings{
peng2024swapnas,
title={{SWAP}-{NAS}: Sample-Wise Activation Patterns for Ultra-fast {NAS}},
author={Yameng Peng and Andy Song and Haytham M. Fayek and Vic Ciesielski and Xiaojun Chang},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=tveiUXU2aa}
}