Summary of Entropy-informed Weighting Channel Normalizing Flow, by Wei Chen et al.
Entropy-Informed Weighting Channel Normalizing Flow
by Wei Chen, Shian Du, Shigui Li, Delu Zeng, John Paisley
First submitted to arxiv on: 6 Jul 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 paper introduces a novel architecture for Normalizing Flows (NFs) called Entropy-Informed Weighting Channel Normalizing Flow (EIW-Flow). NFs are popular in deep generative models due to their ability to provide exact likelihood estimation and efficient sampling. However, existing architectures have limitations, such as high memory requirements, which the proposed EIW-Flow aims to address. The EIW-Flow integrates a regularized and feature-dependent Shuffle operation that adaptively shuffles latent variables before splitting them at the channel level. This approach guides the variables to evolve in the direction of entropy increase. Experimental results show that the EIW-Flow achieves state-of-the-art density estimation results on CIFAR-10, CelebA, and ImageNet datasets with negligible additional computational overhead. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to improve Normalizing Flows (NFs) for deep learning models. NFs are used to generate realistic images and data. However, they can be memory-hungry and slow. The new method, called EIW-Flow, reduces these limitations by adapting how the information is split and processed. This makes it faster and more efficient without sacrificing accuracy. The results show that EIW-Flow outperforms existing methods on several datasets, including CIFAR-10, CelebA, and ImageNet. |
Keywords
» Artificial intelligence » Deep learning » Density estimation » Likelihood
