AcGAN

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The implement attention conditional GANs (AcGAN) model.

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## Look globally, age locally: Face aging with an attention mechanism (AcGANs)

PyTorch implementation of the AcGANs algorithm in the paper ``[Look globally, age locally: Face aging with an attention mechanism.](http://arxiv.org/abs/1910.12771)''.

### 1. The Architecture of AcGANs

---

![Architecture of AcGAN](https://raw.githubusercontent.com/JensonZhu14/AcGAN/master/images/face_aging_network.png)

### 2. Prerequisites

----

* Python 3.6

* PyTorch 1.3.0
* GPU

### 3. Dataset & Preparation

------

* [Morph](https://ebill.uncw.edu/C20231_ustores/web/classic/product_detail.jsp?PRODUCTID=8)
* [CACD](http://bcsiriuschen.github.io/CARC/_)

### 4. Training

----

Training a model by:

```
$ python main.py config/morph.yml
```

### 5. Results

-----

* Attention Results
* ![attention_results](https://raw.githubusercontent.com/JensonZhu14/AcGAN/master/images/attention_result.png)

* Results on the Morph Dataset

* ![results_on_morph](https://raw.githubusercontent.com/JensonZhu14/AcGAN/master/images/aging_morph_result.png)

* Comparison of AcGANs, IPCGANs, and CAAE in the Morph Dataset

![comparison_result](https://raw.githubusercontent.com/JensonZhu14/AcGAN/master/images/comparison_in_vis.png)

### 6. Citation

-----

Zhu H, Huang Z, Shan H, et al. Look globally, age locally: Face aging with an attention mechanism[J]. arXiv preprint arXiv:1910.12771, 2019.

### 7. License

------

**AcGANs** is freely available for free non-commercial use, and may be redistributed under these conditions. For commercial queries, contact [Junping Zhang](http://www.pami.fudan.edu.cn/~jpzhang/).

AcGAN

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