Summary of Paired Wasserstein Autoencoders For Conditional Sampling, by Moritz Piening and Matthias Chung
Paired Wasserstein Autoencoders for Conditional Sampling
by Moritz Piening, Matthias Chung
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paired Wasserstein autoencoder addresses theoretical difficulties in adapting Wasserstein autoencoders to the conditional case, enabling practical applicability in imaging tasks like denoising, inpainting, and unsupervised image translation. By leveraging pairwise independence and an optimal autoencoder pair, the model overcomes these hurdles. Experiments demonstrate the effectiveness of paired Wasserstein autoencoders for image-to-image translation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a way to create new images that looks like old ones. This is called generative neural network models. One type of this model is called Wasserstein autoencoder. It’s simple and easy to use, but when we try to make it work with conditions, we run into problems. To solve this issue, scientists propose using two paired autoencoders. They tested their idea on some tasks like cleaning up noisy pictures or filling in missing parts of images. This new model can also translate one image to another. |
Keywords
» Artificial intelligence » Autoencoder » Neural network » Translation » Unsupervised
