Interpretability of Deep Learning Article Swipe

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Zhenlin Huang , Fan Li , Zhanliang Wang , Zhiyuan Wang ·

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.18178/ijfcc.2022.11.2.585

Deep Learning achieves surprising performance in many real-world tasks. However, on a black-box approach, computational techniques have been applied without a strong critical understanding of their properties. In this paper, we review the current methodologies and techniques about improving the interpretability of Deep Learning from different research directions. Some works are based on analysis of the learning process, some lay more emphasis on interpreted network architecture, and others intend to design self-interpretable Deep Learning models. This article analyzes the popular and advanced works in these fields and provides a future look for Deep Learning researchers.

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Computer Science

Deep Learning

Artificial Intelligence

Process (Computing)

Machine Learning

Data Science

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Interpretability Computer science Deep learning Artificial intelligence Black box Process (computing) Machine learning Data science Operating system

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Type: article
Language: en
Landing Page: https://doi.org/10.18178/ijfcc.2022.11.2.585
OA Status: diamond
Cited By: 8
Related Works: 10
OpenAlex ID: https://openalex.org/W4280517929

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OpenAlex ID: https://openalex.org/W4280517929

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DOI: https://doi.org/10.18178/ijfcc.2022.11.2.585

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Title: Interpretability of Deep Learning

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Type: article

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Language: en

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Publication year: 2022

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Publication date: 2022-05-18

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Authors: Zhenlin Huang, Fan Li, Zhanliang Wang, Zhiyuan Wang

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Landing page: https://doi.org/10.18178/ijfcc.2022.11.2.585

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Open access: Yes

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OA status: diamond

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OA URL: https://doi.org/10.18178/ijfcc.2022.11.2.585

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Concepts: Interpretability, Computer science, Deep learning, Artificial intelligence, Black box, Process (computing), Machine learning, Data science, Operating system

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Cited by: 8

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Citations by year (recent): 2025: 3, 2023: 3, 2020: 2

Per-year citation counts (last 5 years)
Related works (count): 10

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