Summary of Sample What You Cant Compress, by Vighnesh Birodkar et al.
Sample what you cant compress
by Vighnesh Birodkar, Gabriel Barcik, James Lyon, Sergey Ioffe, David Minnen, Joshua V. Dillon
First submitted to arxiv on: 4 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 approach combines autoencoder representation learning with diffusion to improve learned image representations. By jointly learning a continuous encoder and decoder under a diffusion-based loss, the method yields better reconstruction quality compared to GAN-based autoencoders while being easier to tune. The resulting representation is also easier to model with a latent diffusion model than state-of-the-art GAN-based losses. Furthermore, the stochastic nature of the decoder allows it to generate details not encoded in the otherwise deterministic latent representation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Our research combines autoencoder representation learning with diffusion to improve learned image representations. We show that this approach yields better results compared to traditional methods while being easier to use. Our method is also easier to understand and can be used to create new images by generating details not seen before. |
Keywords
» Artificial intelligence » Autoencoder » Decoder » Diffusion » Diffusion model » Encoder » Gan » Representation learning
