Quantized Class Incremental Learning
July 17, 2024 ยท View on GitHub
Code for our paper "Hadamard Domain Training with Integers for Class Incremental Quantized Learning" accepted for Third Conference on Lifelong Learning Agents.
Run Commands
No quant baseline CIL:
python3 main.py -model icarl -p benchmark -seed 42467 --dataset="cifar100" --init_cls=20 --incre=20 --model_type="resnet32" --quantMethod="noq"
HDQT with CIL CIFAR100:
python3 main.py -model icarl -p benchmark -seed 42467 --dataset="cifar100" --init_cls=20 --incre=20 --model_type="resnet32" --quantMethod="ours" --quantBits=4 --quantAccBits=8 --quantFWDWgt="int" --quantFWDAct="int" --quantBWDAct="stoch" --quantBWDWgt="int" --quantBWDGrad1="stoch" --quantBWDGrad2="stoch" --quantBlockSize=32
HDQT with CIL HAR-DSADS:
python3 main.py -model icarl -p benchmark -seed 42467 --dataset="dsads" --init_cls=2 --incre=2 --model_type="fcnet" --fc_hid_dim=405 --init_lr=0.01 --lr=0.01 --epochs=100 --init_epoch=100 --memory_size=200 --init_milestones=50 --milestones=50 --quantMethod="ours" --quantBits=4 --quantAccBits=8 --quantFWDWgt="int" --quantFWDAct="int" --quantBWDAct="stoch" --quantBWDWgt="int" --quantBWDGrad1="stoch" --quantBWDGrad2="stoch" --quantBlockSize=32
LuQ [1] with CIL CIFAR100
python3 main.py -model icarl -p benchmark -seed 42467 --dataset="cifar100" --init_cls=20 --incre=20 --model_type="resnet32" --quantMethod="luq_og" --quantBits=4 --quantAccBits=8
Supported CIL methods: icarl, bic, der, lwf, memo, ours
Supported data sets: cifar100, dsads, hapt, pamap
All packages necessary to run commands can be found in requirements.txt
Citation
@article{schiemer2023hadamard,
title={Hadamard Domain Training with Integers for Class Incremental Quantized Learning},
author={Schiemer, Martin and Schaefer, Clemens JS and Vap, Jayden Parker and Horeni, Mark James and Wang, Yu Emma and Ye, Juan and Joshi, Siddharth},
journal={arXiv preprint arXiv:2310.03675},
year={2023}
}
Sources
[1] LuQ https://openreview.net/forum?id=yTbNYYcopd