This is code base for the following paper:
Tz-Ying Wu, Gurumurthy Swaminathan, Zhizhong Li, Avinash Ravichandran, Nuno Vasconcelos, Rahul Bhotika, Stefano Soatto
Please read our paper for details!
To create a conda environment to run the project, simply run
conda env create -f CIL.yml
.
Create a soft link of the imagenet folder (the root folder that includes train/val image folders) at prepro/data/imagenet
.
bash scripts/getresults80040.sh -l layer4 -n 10 # for resnet10
bash scripts/getresults80040.sh -l layer4 -n 18 # for resnet18
bash scripts/getresults80040.sh -l fc -n 10 # for resnet10, fc-only
bash scripts/getresults80040.sh -l fc -n 18 # for resnet18, fc-only
bash scripts/getresults50050.sh -l layer4 -n 10
bash scripts/getresults50050.sh -l layer4 -n 18
bash scripts/getresults50050.sh -l fc -n 10
bash scripts/getresults50050.sh -l fc -n 18
See CONTRIBUTING for more information.
This project is licensed under the Apache-2.0 License.