Syn2Real Transfer Learning for Image Deraining using Gaussian Processes
Rajeev Yasarla*, Vishwanath A. Sindagi*, Vishal M. Patel
Paper Link(CVPR '20)
@InProceedings{Yasarla_2020_CVPR,
author = {Yasarla, Rajeev and Sindagi, Vishwanath A. and Patel, Vishal M.},
title = {Syn2Real Transfer Learning for Image Deraining Using Gaussian Processes},
booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}
We propose a Gaussian Process-based semi-supervised learning framework which enables the network in learning to derain using synthetic dataset while generalizing better using unlabeled real-world images. Through extensive experiments and ablations on several challenging datasets (such as Rain800, Rain200H and DDN-SIRR), we show that the proposed method, when trained on limited labeled data, achieves on-par performance with fully-labeled training. Additionally, we demonstrate that using unlabeled real-world images in the proposed GP-based framework results in superior performance as compared to existing methods.
- Linux
- Python 2 or 3
- Pytorch version >=1.0
- CPU or NVIDIA GPU + CUDA CuDNN (CUDA 8.0)
- download the rain datasets and arrange the rainy images and clean images in the following order
- Save the image names into text file (dataset_filename.txt)
.
├── data
| ├── train # Training
| | ├── derain
| | | ├── <dataset_name>
| | | | ├── rain # rain images
| | | | └── norain # clean images
| | | └── dataset_filename.txt
| └── test # Testing
| | ├── derain
| | | ├── <dataset_name>
| | | | ├── rain # rain images
| | | | └── norain # clean images
| | | └── dataset_filename.txt
- mention test dataset text file in the line 57 of test.py, for example
val_filename = 'SIRR_test.txt'
- Run the following command
python test.py -category derain -exp_name DDN_SIRR_withGP
- mention the labeled, unlabeled, and validation dataset in lines 119-121 of train.py, for example
labeled_name = 'DDN_100_split1.txt'
unlabeled_name = 'real_input_split1.txt'
val_filename = 'SIRR_test.txt'
- Run the following command
python train.py -train_batch_size 2 -category derain -exp_name DDN_SIRR_withGP -lambda_GP 0.015 -epoch_start 0
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