This repository contains the codes of the following paper
BB Damodaran, B Kellenberger, R Flamary, D Tuia, N Courty, "DeepJDOT:Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation", in European Conference on Computer Vision 2018 (ECCV-2018).
In order to run, the code requires the following Python modules:
- Numpy
- Matplotlib
- POT (Python Optimal Transport library)
- keras with tensorflow backend (preferably GPU version)
- imutils (only for rotating images in regression demo)
- scikit-learn (for scoring functions)
- Deepjdot - module contains the implementation of the DeepJDOT
- dnn - import necessary functions from keras
- deepjdot_demo - DeepJDOT on the sample dataset
- deepjdot_svhn_mnist - DeepJDOT on SVHN & MNIST dataset
To run the DeepJDOT on the sample 2D dataset, please see or run the "deepjdot_demo.py".
To run on the real data set: SVHN --> MNIST, please see "deepjdot_svhn_mnist.py".
The default task is to do classification but you can turn on regression demo by setting do_reg to True.
If you do not want to wait long training time you can set small_model to True.
For regression demo, each image will be randomly rotated around its center, and then the label will be the angle rotated. The angle will be scaled to [0, 1]. The model needs to predict how much the image is rotated.
I suggest you run the demo files inside Spyder or any interactive python IDE so that you can investigate
each cell denoted by #%% lines and understand the code better.