The full paper can be found in here: https://arxiv.org/abs/1905.06526

Results on Generative Model	Data Social Network

• Python 2.7
• TensorFlow >= 1.2rc0
• Numpy
• Tensorflow slim library [https://github.com/tensorflow/models/tree/master/research/slim] (for inception v3 architecture, you can use custom built network architecture if you have one. Since the five datasets shown in the paper have different number of classes, I added 4 more last layers for inception v3. Please make changes based on your need.)

• images.py: Script to run the training process.
• utils.py: Utility functions.
• datagenerator.py: Contains a wrapper class for the new input pipeline.
• images/*: contains some teaser images from the paper.

All you need to touch is the images.py. You can configure different parameters in there. You have to provide .txt files to the script (exp_1_train.txt, exp_2_train.txt, .... and exp_1_test.txt, exp_2_test.txt, .... for different datasets. In the paper, I used five datasets.) Each of them list the complete path to your train/val images together with the class number in the following structure.

Example train.txt:
/path/to/train/image1.png 0
/path/to/train/image2.png 1
/path/to/train/image3.png 2
/path/to/train/image4.png 0
.
.

were the first column is the path and the second the class label.

In the paper and in the current training script, I used five datasets:

1. Caltech-UCSD Birds 200
2. Stanford Dogs Dataset
3. Flower Datasets
4. Cars Dataset
5. FGVC-Aircraft Benchmark