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On an Interpretation of ResNets via Gate-Network Control
1Shuitu Institute of Applied Mathematics, Chongqing 400700, P.R.C. cchuang@mail.ustc.edu.cn.
Neural Computation
|July 12, 2023
Summary
This study presents a novel interpretation of Residual Networks (ResNets) for multicategory classification, drawing parallels with Long Short-Term Memory (LSTM) gate control. This framework explains ResNet performance and demonstrates their universal approximation capabilities.
Area of Science:
- Computer Science
- Artificial Intelligence
- Machine Learning
Background:
- Residual Networks (ResNets) are widely used for deep learning tasks.
- Understanding the underlying mechanisms of ResNet architectures is crucial for further advancements.
- Multicategory classification remains a challenging problem in machine learning.
Purpose of the Study:
- To construct a typical solution for multicategory classification using ResNets.
- To provide a general interpretation of the ResNet architecture and its performance mechanism.
- To explore the universal-approximation capability of a specific ResNet architecture.
Main Methods:
- Constructing a ResNet solution inspired by the gate control mechanism of Long Short-Term Memory (LSTM) networks.
- Applying this interpretation to multicategory classification tasks.
- Analyzing the performance of ResNets with two-layer gate networks.
Main Results:
- A general interpretation of the ResNet architecture and its performance mechanism was established.
- The generality of this interpretation was demonstrated through multiple solutions.
- The universal-approximation capability of ResNets with two-layer gate networks was shown.
Conclusions:
- The proposed interpretation offers a new perspective on ResNet functionality.
- ResNets, particularly those with two-layer gate networks, possess significant theoretical and practical importance for classification.
- This work contributes to a deeper understanding of deep learning architectures.
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