This is the official repo for ICLR 2024 Paper "SAS: Structured Activation Sparsification" Yusuke Sekikawa and Shingo Yashima

paper, openreview

SAS expands representational capacity by projecting inputs into a higher-dimensional, structured-sparse space.This is equivalent to selecting the appropriate weights depending on its input—thereby improving accuracy without increasing FLOPs.

All models were trained on the ImageNet dataset under the following common settings:

• Loss function: Distillation loss
• Data augmentation: AutoAugment
• Batch size: 256
• Learning rate scheduler: Cosine annealing
• Optimizer: SGD (momentum=0.9, weight decay=1e-4)

※ Wide ResNet-18 refers to a model that has twice as many parameters as ResNet-18.

• SAS achieves a 46.0% reduction in FLOPs at a fixed Top-1 accuracy.
  • The width (X≒1.8) that matches SAS’s Top-1 accuracy was determined by linearly interpolating between the Top-1 accuracies of ResNet-18 and Wide ResNet-18. We then set this Wide (X≒1.8) ResNet-18 as the 100 % FLOPs baseline and calculated the relative FLOPs of SAS accordingly.

• SAS increases Top-1 accuracy by 2.63% at the same FLOPs.

python train.py <TRAIN_DATA_DIR> <VAL_DATA_DIR>

python train.py <TRAIN_DATA_DIR> <VAL_DATA_DIR> --use_sas

python train.py <TRAIN_DATA_DIR> <VAL_DATA_DIR> --arch wide_resnet18

python train.py <TRAIN_DATA_DIR> <VAL_DATA_DIR> --use_sas --arch wide_resnet18

If you find our code or paper useful, please cite the following:

@inproceedings{
sekikawa2024sas,
title={{SAS}: Structured Activation Sparsification},
author={Yusuke Sekikawa and Shingo Yashima},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=vZfi5to2Xl}
}