Fetching the paper…
Reading the bibliography…
We introduce ProtoPool, an interpretable image classification model with a pool of prototypes shared by the classes.
Neisser, U.: Cognitive psychology (new york: Appleton). Century, Crofts (1967)
1967
Earlier work this paper cites.
Kesner, R.: A neural system analysis of memory storage and retrieval. Psychological Bulletin
1973
Earlier work this paper cites.
Luria, A.: The origin and cerebral organization of man’s conscious action. Children with learning problems: Readings in a developmental-interaction. New York, Brunner/Mazel pp. 109–130 (1973)
1973
Earlier work this paper cites.
Rosch, E.H.: Natural categories. Cognitive psychology
1973
Earlier work this paper cites.
Rosch, E.: Cognitive representations of semantic categories. Journal of experimental psychology: General
1975
Earlier work this paper cites.
Fiske, S.T., Taylor, S.E.: Social cognition. Mcgraw-Hill Book Company (1991)
1991
Earlier work this paper cites.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. pp. 248–255. Ieee (2009)
2009
Earlier work this paper cites.
Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: The caltech-ucsd birds-200-2011 dataset (2011)
2011
Earlier work this paper cites.
Zhou, B., Sun, Y., Bau, D., Torralba, A.: Interpretable basis decomposition for visual explanation. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 119–134 (2018)
2011
Earlier work this paper cites.
Krause, J., Stark, M., Deng, J., Fei-Fei, L.: 3d object representations for fine-grained categorization. In: Proceedings of the IEEE international conference on computer vision workshops. pp. 554–561 (2013)
2013
Earlier work this paper cites.
Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: Visualising image classification models and saliency maps. In: In Workshop at International Conference on Learning Representations. Citeseer (2014)
2014
Earlier work this paper cites.
Kingma, D.P., Ba, J.L.: Adam: A method for stochastic optimization. In: ICLR 2015 : International Conference on Learning Representations 2015 (2015)
2015
Earlier work this paper cites.
Xiao, T., Xu, Y., Yang, K., Zhang, J., Peng, Y., Zhang, Z.: The application of two-level attention models in deep convolutional neural network for fine-grained image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 842–850 (2015)
2015
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Ribeiro, M.T., Singh, S., Guestrin, C.: ” why should i trust you?” explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. pp. 1135–1144 (2016)
2016
Earlier work this paper cites.
Fong, R.C., Vedaldi, A.: Interpretable explanations of black boxes by meaningful perturbation. In: Proceedings of the IEEE international conference on computer vision. pp. 3429–3437 (2017)
2017
Earlier work this paper cites.
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 4700–4708 (2017)
2017
Earlier work this paper cites.
Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Proceedings of the 31st international conference on neural information processing systems. pp. 4768–4777 (2017)
2017
Earlier work this paper cites.
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision. pp. 618–626 (2017)
2017
Earlier work this paper cites.
Sundararajan, M., Taly, A., Yan, Q.: Axiomatic attribution for deep networks. In: International Conference on Machine Learning. pp. 3319–3328. PMLR (2017)
2017
Earlier work this paper cites.
Zheng, H., Fu, J., Mei, T., Luo, J.: Learning multi-attention convolutional neural network for fine-grained image recognition. In: Proceedings of the IEEE international conference on computer vision. pp. 5209–5217 (2017)
2017
Earlier work this paper cites.
Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., Kim, B.: Sanity checks for saliency maps. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems. vol. 31. Curran Associates, Inc. (2018),
2018
Cited alongside, same era.
Alvarez Melis, D., Jaakkola, T.: Towards robust interpretability with self-explaining neural networks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems. vol. 31. Curran Associates, Inc. (2018),
2018
Cited alongside, same era.
Kim, B., Wattenberg, M., Gilmer, J., Cai, C., Wexler, J., Viegas, F., et al.: Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In: International conference on machine learning. pp. 2668–2677. PMLR (2018)
2018
Cited alongside, same era.
Guidotti, R., Monreale, A., Matwin, S., Pedreschi, D.: Explaining image classifiers generating exemplars and counter-exemplars from latent representations. Proceedings of the AAAI Conference on Artificial Intelligence
2020
Later among the works it cites.
Koh, P.W., Nguyen, T., Tang, Y.S., Mussmann, S., Pierson, E., Kim, B., Liang, P.: Concept bottleneck models. In: III, H.D., Singh, A. (eds.) Proceedings of the 37th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 119, pp. 5338–5348. PMLR (13–18 Jul 2020),
2020
Later among the works it cites.
Mothilal, R.K., Sharma, A., Tan, C.: Explaining machine learning classifiers through diverse counterfactual explanations. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. pp. 607–617 (2020)
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
Van Horn, G., Mac Aodha, O., Song, Y., Cui, Y., Sun, C., Shepard, A., Adam, H., Perona, P., Belongie, S.: The inaturalist species classification and detection dataset. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 8769–8778 (2018)
2018
Cited alongside, same era.
Brendel, W., Bethge, M.: Approximating CNNs with bag-of-local-features models works surprisingly well on imagenet. In: International Conference on Learning Representations (2019),
2019
Cited alongside, same era.
Chen, C., Li, O., Tao, D., Barnett, A., Rudin, C., Su, J.K.: This looks like that: deep learning for interpretable image recognition. In: NeurIPS. pp. 8930–8941 (2019)
2019
Cited alongside, same era.
Fong, R., Patrick, M., Vedaldi, A.: Understanding deep networks via extremal perturbations and smooth masks. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 2950–2958 (2019)
2019
Cited alongside, same era.
Gee, A.H., Garcia-Olano, D., Ghosh, J., Paydarfar, D.: Explaining deep classification of time-series data with learned prototypes. In: CEUR workshop proceedings. vol. 2429, p. 15. NIH Public Access (2019)
2019
Cited alongside, same era.
Ghorbani, A., Wexler, J., Zou, J.Y., Kim, B.: Towards automatic concept-based explanations. In: Wallach, H., Larochelle, H., Beygelzimer, A., d'Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems. vol. 32. Curran Associates, Inc. (2019),
2019
Cited alongside, same era.
Goyal, Y., Wu, Z., Ernst, J., Batra, D., Parikh, D., Lee, S.: Counterfactual visual explanations. In: International Conference on Machine Learning. pp. 2376–2384. PMLR (2019)
2019
Cited alongside, same era.
Hase, P., Chen, C., Li, O., Rudin, C.: Interpretable image recognition with hierarchical prototypes. In: Proceedings of the AAAI Conference on Human Computation and Crowdsourcing. vol. 7, pp. 32–40 (2019)
2019
Cited alongside, same era.
2020
Later among the works it cites.
Puyol-Antón, E., Chen, C., Clough, J.R., Ruijsink, B., Sidhu, B.S., Gould, J., Porter, B., Elliott, M., Mehta, V., Rueckert, D., et al.: Interpretable deep models for cardiac resynchronisation therapy response prediction. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 284–293. Springer (2020)
2020
Later among the works it cites.
Rebuffi, S.A., Fong, R., Ji, X., Vedaldi, A.: There and back again: Revisiting backpropagation saliency methods. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 8839–8848 (2020)
2020
Later among the works it cites.
Wang, P., Vasconcelos, N.: Scout: Self-aware discriminant counterfactual explanations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 8981–8990 (2020)
2020
Later among the works it cites.
Yeh, C.K., Kim, B., Arik, S., Li, C.L., Pfister, T., Ravikumar, P.: On completeness-aware concept-based explanations in deep neural networks. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.F., Lin, H. (eds.) Advances in Neural Information Processing Systems. vol. 33, pp. 20554–20565. Curran Associates, Inc. (2020),
2020
Later among the works it cites.
Afnan, M.A.M., Liu, Y., Conitzer, V., Rudin, C., Mishra, A., Savulescu, J., Afnan, M.: Interpretable, not black-box, artificial intelligence should be used for embryo selection. Human Reproduction Open (2021)
2021
Closest in time.
2021
Closest in time.
Basaj, D., Oleszkiewicz, W., Sieradzki, I., Górszczak, M., Rychalska, B., Trzcinski, T., Zielinski, B.: Explaining self-supervised image representations with visual probing. In: International Joint Conference on Artificial Intelligence (2021)
2021
Closest in time.
2021
Closest in time.
Kaminski, M.E.: The right to explanation, explained. In: Research Handbook on Information Law and Governance. Edward Elgar Publishing (2021)
2021
Closest in time.
Kim, E., Kim, S., Seo, M., Yoon, S.: Xprotonet: Diagnosis in chest radiography with global and local explanations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 15719–15728 (2021)
2021
Closest in time.
Liu, N., Zhang, N., Wan, K., Shao, L., Han, J.: Visual saliency transformer. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 4722–4732 (2021)
2021
Closest in time.
Nauta, M., et al.: Neural prototype trees for interpretable fine-grained image recognition. In: CVPR. pp. 14933–14943 (2021)
2021
Closest in time.
Niu, Y., Tang, K., Zhang, H., Lu, Z., Hua, X.S., Wen, J.R.: Counterfactual vqa: A cause-effect look at language bias. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 12700–12710 (2021)
2021
Closest in time.
Rymarczyk, D., et al.: Protopshare: Prototypical parts sharing for similarity discovery in interpretable image classification. In: SIGKDD. pp. 1420–1430 (2021)
2021
Closest in time.
Singh, G., Yow, K.C.: These do not look like those: An interpretable deep learning model for image recognition. IEEE Access
2021
Closest in time.
Wang, J., et al.: Interpretable image recognition by constructing transparent embedding space. In: ICCV. pp. 895–904 (2021)
2021
Closest in time.
Zhang, Z., Liu, Q., Wang, H., Lu, C., Lee, C.: Protgnn: Towards self-explaining graph neural networks (2022)
2022
Closest in time.