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开源软件名称:Syn2Real开源软件地址:https://gitee.com/mirrors/Syn2Real开源软件介绍:Syn2RealSyn2Real 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. Journal extension:Semi-Supervised Image Deraining using Gaussian Processes Prerequisites:
Dataset structure
. ├── 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 To test Syn2Real:
val_filename = 'SIRR_test.txt'
python test.py -category derain -exp_name DDN_SIRR_withGP To train Syn2Real:
labeled_name = 'DDN_100_split1.txt' unlabeled_name = 'real_input_split1.txt' val_filename = 'SIRR_test.txt'
python train.py -train_batch_size 2 -category derain -exp_name DDN_SIRR_withoutGP -lambda_GP 0.00 -epoch_start 0
python train.py -train_batch_size 2 -category derain -exp_name DDN_SIRR_withGP -lambda_GP 0.0015 -epoch_start 0 -version version1
python train.py -train_batch_size 2 -category derain -exp_name DDN_SIRR_withGP -lambda_GP 0.0015 -epoch_start 0 -version version2 Cross-domain experiments and Gaussian kernelscross domain experiments are performed using DIDMDN dataset as source dataset, and other datasets like Rain800, JORDER_200L, DDN as target datasets. ----------------------------------------------------Source datasets | Target datasets | ----------------------------------------------------DIDMDN | Rain800, JORDER_200L, DDN |---------------------------------------------------- Gaussian processes can be modelled using different kernels like Linear or Squared_exponential or Rational_quadratic. the updated code provides an option to choose the kernel type -kernel_type <Linear or Squared_exponential or Rational_quadratic> Fast version of GPuse GP_new_fast.py file for faster version of GP. To use this GP_new_fast.py : comment line 14 in train.py and uncomment line 15 in train.py Additionally you can use "train_new_comb.py" instead of "train.py". In "train_new_comb.py" does iterative training of the network, i.e. each iteration contains one labeled train step and one unlabeled train step. Run the following command to train Syn2Real (CVPR'20) model using "train_new_comb.py". python train_new_comb.py -train_batch_size 2 -category derain -exp_name DDN_SIRR_withGP -lambda_GP 0.0015 -epoch_start 0 -version version1 |
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