# deep_representation_one_class

**Repository Path**: xdjiangkai/deep_representation_one_class

## Basic Information

- **Project Name**: deep_representation_one_class
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2021-11-26
- **Last Updated**: 2021-11-26

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Deep Representation One-class Classification (DROC).

**This is not an officially supported Google product.**

Tensorflow 2 implementation of the paper: 
[Learning and Evaluating Representations for Deep One-class Classification](https://openreview.net/forum?id=HCSgyPUfeDj) 
published at [ICLR 2021](https://iclr.cc/) as a conference paper
by Kihyuk Sohn, Chun-Liang Li, Jinsung Yoon, Minho Jin, and Tomas Pfister.

This directory contains a two-stage framework for deep one-class classification
example, which includes the self-supervised deep representation learning from
one-class data, and a classifier using generative or discriminative models.

## Install

The `requirements.txt` includes all the dependencies for this project, and an
example of install and run the project is given in run.sh.

```bash
$sh deep_representation_one_class/run.sh
```

## Download datasets

`script/prepare_data.sh` includes an instruction how to prepare data for
CatVsDog and CelebA datasets. For CatVsDog dataset, the data needs to be
downloaded manually. Please uncomment line 2 to set `DATA_DIR` to download
datasets before starting it.

## Run

The options for the experiments are specified thru the command line arguments.
The detailed explanation can be found in `train_and_eval_loop.py`. Scripts for
running experiments can be found

-   Rotation prediction: `script/run_rotation.sh`

-   Contrastive learning: `script/run_contrastive.sh`

-   Contrastive learning with distribution augmentation:
    `script/run_contrastive_da.sh`

## Evaluation

After running `train_and_eval_loop.py`, the evaluation results can be found in
`$MODEL_DIR/stats/summary.json`, where `MODEL_DIR` is specified as model_dir of
`train_and_eval_loop.py`.

## Contacts

kihyuks@google.com, chunliang@google.com, jinsungyoon@google.com,
minhojin@google.com, tpfister@google.com