Summary of Unified Generation, Reconstruction, and Representation: Generalized Diffusion with Adaptive Latent Encoding-decoding, by Guangyi Liu et al.
Unified Generation, Reconstruction, and Representation: Generalized Diffusion with Adaptive Latent Encoding-Decoding
by Guangyi Liu, Yu Wang, Zeyu Feng, Qiyu Wu, Liping Tang, Yuan Gao, Zhen Li, Shuguang Cui, Julian McAuley, Zichao Yang, Eric P. Xing, Zhiting Hu
First submitted to arxiv on: 29 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper introduces Generalized Encoding-Decoding Diffusion Probabilistic Models (EDDPMs) that integrate three core capabilities of deep generative models: generating new instances, reconstructing inputs, and learning compact representations. EDDPMs generalize the Gaussian noising-denoising in standard diffusion by introducing parameterized encoding-decoding. This allows for effective learning of the encoder-decoder parameters jointly with diffusion, making it compatible with established diffusion model objectives and training recipes. The paper demonstrates the flexibility of EDDPMs to handle diverse data types (text, proteins, images) and tasks, showing strong improvements over various existing models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to make computers generate new things, like text or pictures. It combines three important skills that deep learning models already have: making new things from scratch, copying what’s given, and finding patterns in data. The new method, called EDDPMs, lets computers learn these skills together instead of separately. This makes it good at doing different tasks with different types of data (like text, proteins, or pictures). It even does better than some other computer programs that do similar things. |
Keywords
* Artificial intelligence * Deep learning * Diffusion * Diffusion model * Encoder decoder
