Derivative Manipulation for General Example Weighting
For source codes, the usage is conditioned on academic use only and kindness to cite our work: Derivative Manipulation and IMAE.
As a young researcher, your interest and kind citation (star) will definitely mean a lot for me and my collaborators.
For any specific discussion or potential future collaboration, please feel free to contact me.
@article{wang2019derivative,
title={Derivative Manipulation for General Example Weighting},
author={Wang, Xinshao and Kodirov, Elyor and Hua, Yang and Robertson, Neil M},
journal={arXiv preprint arXiv:1905.11233},
year={2019}
}
Downloading Link
https://www.dropbox.com/sh/iy79ixgmtnht9qw/AAA_tQvtAi8Xy30HqPe0mGiAa?dl=0
Command for Visualising the Repository/Directory Tree Structure
cd directory_name
tree
Dependencies
The core functions are implemented in the caffe framework. We use matlab interfaces matcaffe for data preparation.
Setup
- Install dependencies on Ubuntu 16.04
sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler sudo apt-get install --no-install-recommends libboost-all-dev sudo apt-get install libopenblas-dev sudo apt-get install python-dev sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
Install MATLAB 2017b
Download and Run the install binary file
./install
Compile Caffe and matlab interface
Note you may need to change some paths in Makefile.config according your system environment and MATLAB path
cd CaffeMex_CCE_sumW make -j8 && make matcaffe cd ../CaffeMex_UnifiedWeight_V01 make -j8 && make matcaffe cd ../CaffeMex_GCE make -j8 && make matcaffe cd ../CaffeMex_GCE_sumW make -j8 && make matcaffe cd ../CaffeMex_MAE_sumW make -j8 && make matcaffe cd ../CaffeMex_MAE_V00 make -j8 && make matcaffe cd ../CaffeMex_MSE make -j8 && make matcaffe cd ../CaffeMex_MSE_sumW make -j8 && make matcaffe
Usage
Examples for reproducing our results on CIFAR-100 are given.
Data preparation for CIFAR-100
- Prepare testing data:
cd CIFAR100_Data_Toolkit matlab -nodisplay -nosplash -nodesktop -r "run('test_data_preparation.m');exit;" | tail -n +11
- Prepare training data (symmetric noise rate: 0.0, 0.2, 0.4, 0.6):
matlab -nodisplay -nosplash -nodesktop -r "run('train_data_preparationV2_noise_0_0.m');exit;" | tail -n +11 matlab -nodisplay -nosplash -nodesktop -r "run('train_data_preparationV2_noise_0_2.m');exit;" | tail -n +11 matlab -nodisplay -nosplash -nodesktop -r "run('train_data_preparationV2_noise_0_4.m');exit;" | tail -n +11 matlab -nodisplay -nosplash -nodesktop -r "run('train_data_preparationV2_noise_0_6.m');exit;" | tail -n +11
- Copy data
cd .. echo CIFAR100_ResNet44*/pre_pro_process | xargs -n 1 cp CIFAR100_Data_Toolkit/TestImageDataCell.mat echo CIFAR100_ResNet44*/pre_pro_process | xargs -n 1 cp CIFAR100_Data_Toolkit/TrainImageDataCell0.0.mat echo CIFAR100_ResNet44*/pre_pro_process | xargs -n 1 cp CIFAR100_Data_Toolkit/TrainImageDataCell0.2.mat echo CIFAR100_ResNet44*/pre_pro_process | xargs -n 1 cp CIFAR100_Data_Toolkit/TrainImageDataCell0.4.mat echo CIFAR100_ResNet44*/pre_pro_process | xargs -n 1 cp CIFAR100_Data_Toolkit/TrainImageDataCell0.6.mat
- Prepare testing data:
Train & Test
Run the training and testing scripts in the training folder of a specific setting defined by its corresponding prototxt folder.
For example,
cd CIFAR100_ResNet44_V03_lambda0_5/train_Res44_USW_Beta06_lambda1_0.0 matlab -nodisplay -nosplash -nodesktop -r "run('train.m');exit;" | tail -n +11 matlab -nodisplay -nosplash -nodesktop -r "run('test.m');exit;" | tail -n +11
Our trained results
- Our trained results are stored in corresponding folders. For example, in Folder CIFAR100_ResNet44_V03_lambda0_5 train_Res44_USW_Beta06_lambda1_0.0, there are:
- accuracy.txt
- accuracy_curve.png
- Without changing the random seed (123), you are supposed to obtain exactly the same results.
Acknowledgements
Our implementation benefits from:
Caffe library: https://caffe.berkeleyvision.org/
CaffeMex_v2 library: https://github.com/sciencefans/CaffeMex_v2/tree/9bab8d2aaa2dbc448fd7123c98d225c680b066e4