Stanislav Pidhorskyi • Donald A. Adjeroh • Gianfranco Doretto

Official repository of the paper

Google Drive folder with models and qualitative results

Introduction to Adversarial Latent Autoencoders (ALAE)

The Adversarial Latent Autoencoders (ALAE) present a powerful approach to enhance generative models through the synergy of autoencoder architecture and adversarial training. This architecture addresses key issues such as the generative power of GANs and the disentangled representations within autoencoders.

ALAE consists of two types of autoencoders: one uses a Multi-Layer Perceptron (MLP) encoder, while the other, named StyleALAE, utilizes a StyleGAN generator. The key innovation of ALAE is its potential to not only generate high-quality images but also manipulate and reconstruct them effectively.

How to Run ALAE Demo

To run the ALAE demo successfully, follow these steps:

1. 1. Ensure your environment is set up with a CUDA-capable GPU, PyTorch version 1.3.1, and the appropriate cuDNN drivers.
   2. Install the required packages using the command:

pip install -r requirements.txt

1. 1. Download pre-trained models with the following command:

python training_artifacts/download_all.py

1. 1. To run the demo, execute:

python interactive_demo.py

You can specify a YAML configuration file by using the -c parameter to change the default dataset to others like CelebA-HQ.

Understanding the Code via Analogy

Think of the ALAE system as a kitchen where different chefs (the MLP encoder and StyleGAN generator) prepare various dishes (images). Each chef has a unique style and way of preparing their meals. The MLP encoder is like a traditional chef who follows classic recipes, while the StyleGAN generator is akin to a modern chef who uses innovative techniques.

Together, they bring out the best of both worlds—traditional and modern cooking. The ALAE architecture takes advantage of both chefs’ styles (the strengths of the encoder and generator) to create not just one delicious dish (coherent images) but also to combine and modify different ingredients (manipulated images) to cater to customers’ tastes (the users). This duality makes ALAE a culinary marvel in the realm of deep learning.

Troubleshooting

If you encounter issues while setting up or running the ALAE demo, consider the following troubleshooting suggestions:

• Ensure that your GPU drivers and CUDA toolkit are installed and compatible with the versions required by PyTorch.
• Check if the PYTHONPATH is correctly set to the root of the repository if running scripts from the command line.
• If you face download issues regarding pre-trained models, try deleting all *.pth files, update the dlutils package, and then rerun the model download script.

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.