A Study on Explainability of Deep Learning Model for Image Classification Using CycleGAN

Author:

Nakajima Taiga,Yoshida Shinichi

Publisher

Springer Nature Singapore

Reference11 articles.

1. Gao, B.Z.D.C.J., Jiang, Q.: Convolutional neural networks for computer-aided detection or diagnosis in medical image analysis: an overview. Math. Biosci. Eng. 16, 6536–6561 (2019)

2. Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2921–2929 (2016)

3. Yasuyuki, T., Shinomiya, Y., Yoshida, S.: Analysis of trained convolutional neural network using generative adversarial network. In: International Workshop on Advanced Computational Intelligence and Intelligent Informatics(IWACIII), Oct. 31-Nov.3 (2021)

4. Zhu, J.-Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: The Institute of Electrical and Electronics Engineers(IEEE) International Conference on Computer Vision (ICCV) (2017)

5. Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments, Technical Report, University of Massachusetts, Amherst (2007)

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