Summary of Addressing Spectral Bias Of Deep Neural Networks by Multi-grade Deep Learning, By Ronglong Fang and Yuesheng Xu
Addressing Spectral Bias of Deep Neural Networks by Multi-Grade Deep Learning
by Ronglong Fang, Yuesheng Xu
First submitted to arxiv on: 21 Oct 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 proposed Deep Neural Network (DNN) approach addresses the spectral bias issue, where DNNs tend to prioritize lower-frequency components over higher-frequency features. By observing that composition of low-frequency functions can approximate high-frequency functions, the MGDL model is introduced. This multi-grade deep learning model trains a DNN incrementally, grade by grade, using SNNs as features. The authors apply MGDL to synthetic, manifold, colored images, and MNIST datasets, all featuring high-frequency information. Experimental results show that MGDL excels at representing functions with high-frequency information, capturing low-frequency information in each grade and composing SNNs effectively. This study confirms the proposed method’s promise in addressing DNN spectral bias limitations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem with Deep Neural Networks (DNNs) that makes them bad at learning certain details. Instead of trying to learn everything at once, this new approach breaks it down into smaller steps. Each step learns simple things and then combines those learned things to learn more complex things. The authors tested this idea on different types of images and data sets. They found that this method works well for learning about high-frequency features, which are important in many applications. |
Keywords
» Artificial intelligence » Deep learning » Neural network
