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Source code and datasets for the paper "Semi-supervised Compatibility Learning Across Categories for Clothing Matching" (ICME-2019)
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Config.py
Evaluation.py
Generate_tsne.py
README.md
choose_by_cate.py
cloth_match_czy.py
distribution_analysis.py
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README.md

SCGAN

code for "Semi-supervised Compatibility Learning Across Categories for Clothing Matching" in ICME2019

Paper data and code

This is the code for the ICME 2019 Paper: Semi-supervised Compatibility Learning Across Categories for Clothing Matching. We have implemented our methods in Tensorflow.

Here are two datasets we used in our paper. After downloaded the datasets, you can put them in the folder data/:

Usage

You need to run the file data/preprocess.py first to preprocess the data.

For example: cd datasets; python preprocess.py --dataset=sample ('preprocess.py' is still on arrangement.)

usage: preprocess.py [-h] [--dataset DATASET]

Then you can run the file `./clothing_matching.py` to train the model.

For example: `cd pytorch_code; python main.py --dataset=sample`

You can add the suffix `--nonhybrid` to use the global preference of a session graph to recommend instead of the hybrid preference.

You can also change other parameters according to the usage in the file './config.py':

```bash

## Requirements

- Python 2.7
- Tensorflow 1.5.0

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