Interpreting Deep Visual Representations via Network Dissection

Interpreting Deep Visual Representations via Network Dissection

The success of recent deep convolutional neural networks (CNNs) depends on learning hidden representations that can summarize the important factors of variation behind the data. In this work, we describe Network Dissection, a method that interprets networks by providing meaningful labels to their in...

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Bibliographic Details
Main Authors: Zhou, Bolei, Bau, David, Oliva, Aude, Torralba, Antonio
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers 2019
Subjects:
Computational Theory and Mathematics
Software
Applied Mathematics
Artificial Intelligence
Computer Vision and Pattern Recognition
Online Access:https://hdl.handle.net/1721.1/122817

Internet

https://hdl.handle.net/1721.1/122817

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