Visual Interpretability for Deep Learning: a Survey

2 Feb 2018 Quanshi Zhang Song-Chun Zhu

This paper reviews recent studies in understanding neural-network representations and learning neural networks with interpretable/disentangled middle-layer representations. Although deep neural networks have exhibited superior performance in various tasks, the interpretability is always the Achilles' heel of deep neural networks... (read more)

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Methods used in the Paper


METHOD TYPE
Interpretability
Image Models