[CVPR2020] Adversarial Latent Autoencoders

Stanislav Pidhorskyi • Donald A. Adjeroh • Gianfranco Doretto

Official repository of the paper

Google Drive folder with models and qualitative results

# ALAE > **Adversarial Latent Autoencoders**
> Stanislav Pidhorskyi, Donald Adjeroh, Gianfranco Doretto
> > **Abstract:** *Autoencoder networks are unsupervised approaches aiming at combining generative and representational properties by learning simultaneously an encoder-generator map. Although studied extensively, the issues of whether they have the same generative power of GANs, or learn disentangled representations, have not been fully addressed. We introduce an autoencoder that tackles these issues jointly, which we call Adversarial Latent Autoencoder (ALAE). It is a general architecture that can leverage recent improvements on GAN training procedures. We designed two autoencoders: one based on a MLP encoder, and another based on a StyleGAN generator, which we call StyleALAE. We verify the disentanglement properties of both architectures. We show that StyleALAE can not only generate 1024x1024 face images with comparable quality of StyleGAN, but at the same resolution can also produce face reconstructions and manipulations based on real images. This makes ALAE the first autoencoder able to compare with, and go beyond the capabilities of a generator-only type of architecture.* ## Citation * Stanislav Pidhorskyi, Donald A. Adjeroh, and Gianfranco Doretto. Adversarial Latent Autoencoders. In *Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR)*, 2020. [to appear] > @InProceedings{pidhorskyi2020adversarial, author = {Pidhorskyi, Stanislav and Adjeroh, Donald A and Doretto, Gianfranco}, booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR)}, title = {Adversarial Latent Autoencoders}, year = {2020}, note = {[to appear]}, }

preprint on arXiv: 2004.04467

## To run the demo To run the demo, you will need to have a CUDA capable GPU, pytorch >= v1.3.1 and cuda/cuDNN drivers installed. Install required packages: pip install -r requirements.txt Download pretrained models: python training_artifacts/download_all.py Run the demo: python interactive_demo.py You can specify **yaml** config to use. Configs are located here: https://github.com/podgorskiy/ALAE/tree/master/configs. By default, it uses one for FFHQ dataset. You can change the config using `-c` parameter. To run on `celeb-hq` in 256x256 resolution, run: python interactive_demo.py -c celeba-hq256 However, for configs other then FFHQ, you will need to obtain new principal direction vectors for the attibutes.