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Interpretability of Deep Neural Networks has become a major area of exploration.
A. Krizhevsky, Learning Multiple Layers of Features from Tiny Images, https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf (2009)
2009
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J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, L. Fei-Fei, Imagenet: A Large - Scale Hierarchical Image Database, in: Proc. of the IEEE Conf. on Comput. Vis. and Pattern Recognit., 2009, pp. 248–255
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A. Krizhevsky, I. Sutskever, G. E. Hinton, Imagenet Classification with Deep Convolutional Neural Networks, in: Adv. in Neural Inf. Process. Syst., 2012, pp. 1097–1105
2012
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M. D. Zeiler, R. Fergus, Visualizing and Understanding Convolutional Networks, in: Proc. of the Eur. Conf. on Comput. Vis., 2014
2014
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C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, R. Fergus, Intriguing Properties of Neural Networks, in: Proc. of the International Conf. on Learn. Represent., 2014
2014
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K. Simonyan, A. Vedaldi, A. Zisserman, Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps, in: Proc. of the International Conf. on Learn. Represent., 2014
2014
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C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, Going Deeper with Convolutions, in: Proc. of the IEEE Conf. on Comput. Vis. and Pattern Recognit., 2015, pp. 1–9
2015
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A. Choromańska, M. Henaff, M. Mathieu, G. B. Arous, Y. LeCun, The Loss Surfaces of Multilayer Networks, in: Proc. of the International Conf. on Artif. Intell. and Stat., Vol. 38, 2015, pp. 192–204
2015
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S. Bach, A. Binder, G. Montavon, F. Klauschen, K. Müller, W. Samek, On Pixel - Wise Explanations for Non - Linear Classifier Decisions by Layer - Wise Relevance Propagation, in: Plos One, 2015
2015
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B. Liu, M. Wang, H. Foroosh, M. Tappen, M. Pensky, Sparse Convolutional Neural Networks, in: Proc. of the IEEE Conf. on Comput. Vis. and Pattern Recognit., 2015, pp. 806–814
2015
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S. Ioffe, C. Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, in: Proc. of the International Conf. on Mach. Learn., 2015
2015
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K. He, X. Zhang, S. Ren, J. Sun, Deep Residual Learning for Image Recognition, in: Proc. of the IEEE Conf. on Comput. Vis. and Pattern Recognit., 2016, pp. 770–778
2016
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L. A. Hendricks, Z. Akata, M. Rohrbach, J. Donahue, B. Schiele, T. Darrell, Generating Visual Explanations, in: Proc. of the Eur. Conf. on Comput. Vis., 2016
2016
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B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, A. Torralba, Learning Deep Features for Discriminative Localization, in: Proc. of the IEEE Conf. on Comput. Vis. and Pattern Recognit., IEEE, 2016
2016
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S. Zagoruyko, N. Komodakis, Wide Residual Networks, in: Proc. of the Br. Mach. Vis. Conf., 2016
2016
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G. Huang, Z. Liu, K. Q. Weinberger, Densely Connected Convolutional Networks, in: Proc. of the IEEE Conf. on Comput. Vis. and Pattern Recognit., 2017, pp. 2261–2269
2017
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N. Frosst, G. E. Hinton, Distilling a Neural Network into a Soft Decision Tree, in: Cex@ai*ia, 2017
2017
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A. Gonzalez-Garcia, D. Modolo, V. Ferrari, Do Semantic Parts Emerge in Convolutional Neural Networks?, International J. of Comput. Vis. 126 (2017) 476–494
2017
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D. Bau, B. Zhou, A. Khosla, A. Oliva, A. Torralba, Network Dissection - Quantifying Interpretability of Deep Visual Representations., in: Proc. of the IEEE Conf. on Comput. Vis. and Pattern Recognit., 2017, pp. 3319–3327
S. Du, X. Zhai, B. Póczos, A. Singh, Gradient Descent Provably Optimizes over - Parameterized Neural Networks, in: Proc. of the International Conf. on Learn. Represent., 2019
2019
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S. Du, J. Lee, H. Li, L. Wang, X. Zhai, Gradient Descent Finds Global Minima of Deep Neural Networks, in: Proc. of the International Conf. on Mach. Learn., 2019
2019
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M. Tan, Q. V. Le, Efficientnet: Rethinking Model Scaling for Convolutional Neural Networks, in: Proc. of the International Conf. on Mach. Learn., 2019
2019
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J. Yang, X. Shen, J. Xing, X. Tian, H. Li, B. Deng, J. Huang, X. Hua, Quantization Networks, in: Proc. of the IEEE Conf. on Comput. Vis. and Pattern Recognit., 2019
2019
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Q. Zhang, Y. Yang, Y. Wu, S.-C. Zhu, Interpreting CNNs via Decision Trees, in: Proc. of the IEEE Conf. on Comput. Vis. and Pattern Recognit., 2019, pp. 6254–6263
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2017
Cited alongside, same era.
G. Alain, Y. Bengio, Understanding Intermediate Layers Using Linear Classifier Probes, in: Proc. of the International Conf. on Learn. Represent., 2017
2017
Cited alongside, same era.
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, D. Batra, Grad - CAM: Visual Explanations from Deep Networks via Gradient - Based Localization, in: Proc. of the IEEE International Conf. on Comput. Vis., 2017
2017
Cited alongside, same era.
H. Li, A. Kadav, I. Durdanovic, H. Samet, H. Graf, Pruning Filters for Efficient ConvNets, in: Proc. of the International Conf. on Learn. Represent., 2017
2017
Cited alongside, same era.
Z. Jiang, Y. Wang, L. Davis, W. Andrews, V. Rozgic, Learning Discriminative Features via Label Consistent Neural Network, in: Proc. of the IEEE Winter Conf. on Appl. of Comput. Vis., 2017, pp. 207–216
2017
Cited alongside, same era.
B. Zoph, Q. V. Le, Neural Architecture Search with Reinforcement Learning, in: Proc. of the International Conf. on Learn. Represent., 2017
2017
Cited alongside, same era.
Q. Zhang, Y. Wu, S.-C. Zhu, Interpretable Convolutional Neural Networks, in: Proc. of the IEEE Conf. on Comput. Vis. and Pattern Recognit., 2018, pp. 8827–8836
2018
Cited alongside, same era.
Q. Zhang, R. Cao, F. Shi, Y. Wu, S. Zhu, Interpreting CNN Knowledge via an Explanatory Graph, in: Proc. of the AAAI Conf. on Artif. Intell., 2018
2018
Cited alongside, same era.
2019
Later among the works it cites.
S. Wickramanayake, W. Hsu, M. Lee, FLEX: Faithful Linguistic Explanations for Neural Net Based Model Decisions, in: Proc. of the AAAI Conf. on Artif. Intell., 2019, pp. 2539–2546
2019
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Y. Li, S. Lin, B. Zhang, J. Liu, D. Doermann, Y. Wu, F. Huang, R. Ji, Exploiting Kernel Sparsity and Entropy for Interpretable CNN Compression, in: Proc. of the IEEE Conf. on Comput. Vis. and Pattern Recognit., 2019
2019
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T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, D. Amodei, Language Models Are Few - Shot Learners, in: Adv. in Neural Inf. Process. Syst., 2020
2020
Later among the works it cites.
Y. Zhang, P. Tiño, A. Leonardis, K. Tang, A Survey on Neural Network Interpretability, IEEE Trans. on Emerging Topics in Computational Intell. (2020)
2020
Later among the works it cites.
H. Liang, Z. Ouyang, Y. Zeng, H. Su, Z. He, S.-T. Xia, J. Zhu, B. Zhang, Training Interpretable Convolutional Neural Networks by Differentiating Class - Specific Filters, in: Proc. of the Eur. Conf. on Comput. Vis., Springer International Publishing, 2020, pp. 622–638
2020
Later among the works it cites.
S. Lin, R. Ji, Y. Li, C. Deng, X. Li, Toward Compact ConvNets via Structure - Sparsity Regularized Filter Pruning, in: IEEE Trans. on Neural Networks and Learn. Syst., 2020
2020
Later among the works it cites.
S. Wickramanayake, W. Hsu, M. Lee, Comprehensible Convolutional Neural Networks via Guided Concept Learning, in: IJCNN, 2021
2021
Closest in time.
A. Kumar, A. M. Shaikh, Y. Li, H. Bilal, B. Yin, Pruning Filters with L1 - Norm and Capped L1 - Norm for CNN Compression, Applied Intell. 51 (2021) 1152–1160
2021
Closest in time.