Learning To Stop While Learning To Predict (ICML 2020)
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Authors: Xinshi Chen, Hanjun Dai, Yu Li, Xin Gao, Le Song
If you found this library useful in your research, please consider citing
@inproceedings{chen2020learning,
title={Learning to stop while learning to predict},
author={Chen, Xinshi and Dai, Hanjun and Li, Yu and Gao, Xin and Song, Le},
booktitle={International Conference on Machine Learning},
pages={1520--1530},
year={2020},
organization={PMLR}
}
@article{chen2020learning,
title={Learning to Stop While Learning to Predict},
author={Chen, Xinshi and Dai, Hanjun and Li, Yu and Gao, Xin and Song, Le},
journal={arXiv preprint arXiv:2006.05082},
year={2020}
}
Reproduce Experiments In Sec 5.1. Sparse Recovery
Install the module
Please navigate to the root of this repository, and run the following command to install the lista_stop module.
pip install -e .
Run traditional algorithms: ISTA and FISTA
Navigate to the /lista_stop/baselines folder and run the following commands to reproduce results of ISTA and FISTA, respectively.
sh run_ista.sh
sh run_fista.sh
Run the baseline model: LISTA
Navigate to the /lista_stop/experiments folder and run the following command.
sh run_lista.sh
Run our method: LISTA-stop
The training process of LISTA-stop has two stages. For stage 1 training, navigate to the /lista_stop/experiments folder and run the following command.
run_lista_stop_stage1.sh
For stage 2 training, run the following command.
run_lista_stop_stage2.sh
Reproduce Experiments In Sec 5.2. MAML
Please navigate to maml_stop/ folder for the details.
Reproduce Experiments In Sec 5.3. Image Denoising
Configure the environment
Please navigate to the section folder ./dncnn_stop. Then, using the following command, we can configure the environment for the denoise experiments. Please keep the environment activated for this section.
conda env create -f environment.yml
source activate dncnn_stop
Download the dataset
Please download the dataset and unzip the dataset in this folder.
Run our method
Please run the following command to check our method.
# pretrain the model
python -u train.py --model DnCNN --outf logs/dncnn_b_l20_all_train_n55 \
--num_of_layers 20 --batchSize 256 --epoch 50
# fine-tuning with tao as 10
python -u train.py --model DnCNN_DS --outf logs/dncnn_b_ds_l20_all_train_tune_tao10 \
--train_all True --batchSize 256 --lr 1e-4 --epoch 50 \
--tao 10 --pretrain_path logs/dncnn_b_l20_all_train_n55/net.pth \
--pretrain True
# policy training, this is in test phase
python train_stop_kl.py \
--outf logs/dncnn_b_ds_l20_all_train_tune_tao10 --restart True -phase test
# joint training, this is in test phase
python train_stop_joint.py \
--outf logs/dncnn_b_ds_l20_all_train_tune_tao10 --restart True -phase test
# Quantitative evaluation
for noise in 35 45 55 65 75; do
python -u test.py --test_data Set68 --num_of_layers 20 \
--logdir logs/dncnn_b_ds_l20_all_train_tune_tao10 --model DnCNN_DS \
--test_noiseL ${noise}
done
# generate the denoised images
for noise in 45 65;do
python -u test.py --test_data Set68 --num_of_layers 20 \
--logdir logs/dncnn_b_ds_l20_all_train_tune_tao10 --model DnCNN_DS \
--test_noiseL ${noise} --save_img True --img_folder ./out_imgs/dncnn_stop_${noise}
done
Reproduce Experiments In Sec 5.4. Image Recognition
Please navigate to the section folder ./sdn_stop.
Download the dataset
Download TinyImageNet from https://tiny-imagenet.herokuapp.com/, place it under data/ and use data.py - create_val_folder() to generate proper directory structure.
Run the experiment
# Train the classifiers
python train_networks.py
# Check the performance of sdn and l2stop. The policy network would not work at the current stage for this task.
python train_stop_kl.py
Credit and note
We build this part based on http://shallowdeep.network. We mainly changed the loss function in model_funcs.py.
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