Skip to content

Latest commit

 

History

History
86 lines (57 loc) · 2.74 KB

README.md

File metadata and controls

86 lines (57 loc) · 2.74 KB
Raw

Code for the paper Addressing Model Vulnerability to Distributional Shifts over Image Transformation Sets

Overview

The code in this repo allows

  1. Testing the vulnerability of (black-box) models via random search and evolution search over arbitrary transformation sets.

  2. Training more robust models via the RDA/RSDA/ESDA algorithms presented in the paper.

Here a small ConvNet and the MNIST dataset are used, but applying these tools to arbitrary tasks/models is straightforward. Feel free to drop me a message if any feedback can be helpful.

Files

model.py: to build tf's graph

train_ops.py: train/test functions

search_ops.py: search algos (RS/ES from the paper)

transformations_ops.py: modules to build image transformation set and apply transformations

exp_config: config file with the hyperparameters

Some pretrained models are available in a heavier version of this repo.

Prerequisites

Python 2.7, Tensorflow 1.12.0

How it works

To obtain MNIST and SVHN dataset, run

mkdir data
python download_and_process_mnist.py
sh download_svhn.sh

To train the model, run

python main.py --mode=train_MODE --gpu=GPU_IDX --exp_dir=EXP_DIR

where MODE can be one of {ERM, RDA, RSDA, ESDA}, GPU_IDX is the index of the GPU to be used, and EXP_DIR is the folder containing the exp_config file.

To run evolution search (ES) or random search (RS) on a trained model, run

python main.py --mode=test_MODE --gpu=GPU_IDX --exp_dir=EXP_DIR

where MODE can be one of {RS, ES}. For ES, population size POP_SIZE and mutation rate ETA can be set as

python main.py --mode=test_ES --gpu=GPU_IDX --exp_dir=EXP_DIR --pop_size=POP_SIZE --mutation_rate=ETA

To test performance on all digit datasets (MNIST, SVHN, MNIST-M, SYN, USPS), run

python main.py --mode=test_all --gpu=GPU_IDX --exp_dir=EXP_DIR

Testing MNIST-M, SYN and USPS is commented out.

If one desires to include more transformations, or explore different intensity intervals, modifications to transformations_ops.py should be straightforward.

Reference

Addressing Model Vulnerability to Distributional Shifts over Image Transformation Sets
Riccardo Volpi and Vittorio Murino, ICCV 2019

    @InProceedings{Volpi_2019_ICCV,
    author = {Volpi, Riccardo and Murino, Vittorio},
    title = {Addressing Model Vulnerability to Distributional Shifts Over Image Transformation Sets},
    booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
    month = {October},
    year = {2019}
    }