Network dissection: quantifying interpretability of deep visual representations

We propose a general framework called Network Dissection for quantifying the interpretability of latent representations of CNNs by evaluating the alignment between individual hidden units and a set of semantic concepts. Given any CNN model, the proposed method draws on a broad data set of visual con...

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Bibliographic Details
Main Authors: Bau, David (Author), Zhou, Bolei (Author), Khosla, Aditya (Author), Oliva, Aude (Author), Torralba, Antonio (Author)
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor)
Format: Article
Language:English
Published: IEEE, 2020-05-01T19:35:43Z.
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