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About Model-Driven Deep Learning
This document discusses model-driven deep learning, which combines modeling-based and deep learning-based approaches. It begins by noting the successes of deep learning but also its limitations like lack of theoretical foundations and difficulty designing network topology. It then introduces the model-driven approach, which uses models based on task objectives and domain knowledge to guide network design. The rest of the document outlines the process of model-driven deep learning, including constructing a model family based on the task, designing an algorithm family to solve the model family, and using this approach to determine network topology in a way that is theoretically sound while retaining deep learning's ability to learn from data.
Detailed Information
| Author: | ['jun zhao'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 3 |
| Language: | English |
| Format: | |
| Price: | FREE |
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