Summary of Diffusion Bridge Autoencoders For Unsupervised Representation Learning, by Yeongmin Kim et al.
Diffusion Bridge AutoEncoders for Unsupervised Representation Learning
by Yeongmin Kim, Kwanghyeon Lee, Minsang Park, Byeonghu Na, Il-Chul Moon
First submitted to arxiv on: 27 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper introduces a novel approach to diffusion-based representation learning, called DBAE (Diffusion Bridge AuteEncoders), which addresses the issue of information split between the diffusion and auxiliary encoder. The proposed architecture creates an information bottleneck at the latent variable z, allowing for z-dependent endpoint xT inference through a feed-forward structure. This design enables z to hold full information about samples and xT to become a learnable distribution. The paper also proposes an objective function for DBAE that enables both reconstruction and generative modeling, with theoretical justification. Empirically, the approach shows improved downstream inference quality, reconstruction, and disentanglement, as well as high-fidelity sample generation in unconditional generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DBAE is a new way to learn about things using computers. It helps solve a problem where information gets split between two parts of a model. The DBAE method lets the computer understand more about what it’s learning and make better predictions. It also makes it possible for the computer to generate realistic samples. This is important because it can be used in many different areas, like understanding images or videos. |
Keywords
» Artificial intelligence » Diffusion » Encoder » Inference » Objective function » Representation learning
