Visual interpretability for deep learning: a survey

Author:

Zhang Quan-shiORCID,Zhu Song-chun

Funder

the ONR MURI project

the DARPA XAI Award

NSF IIS

Publisher

Zhejiang University Press

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing

Reference47 articles.

1. Aubry M, Russell BC, 2015. Understanding deep features with computer-generated imagery. IEEE Int Conf on Computer Vision, p.2875–2883. https://doi.org/10.1109/ICCV.2015.329

2. Aubry M, Maturana D, Efros A, et al., 2014. Seeing 3D chairs: exemplar part-based 2D–3D alignment using a large dataset of CAD models. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.3762–3769.

3. Bau D, Zhou B, Khosla A, et al., 2017. Network dissection: quantifying interpretability of deep visual representations. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.1063–6919. https://doi.org/10.1109/CVPR.2017.354

4. Chen X, Duan Y, Houthooft R, et al., 2016. Infogan: interpretable representation learning by information maximizing generative adversarial nets. NIPS, p.2172–2180.

5. Dosovitskiy A, Brox T, 2016. Inverting visual representations with convolutional networks. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.4829–4837.

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