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Counterfactual explanations have shown promising results as a post-hoc framework to make image classifiers more explainable.
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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 (ICCV) (Oct 2017)
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Alvarez-Melis, D., Jaakkola, T.S.: Towards robust interpretability with self-explaining neural networks. In: Advances in neural information processing systems (NeurIPS) (2018)
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Arora, S., Risteski, A., Zhang, Y.: Do GANs learn the distribution? some theory and empirics. In: International Conference on Learning Representations (2018), https://openreview.net/forum?id=BJehNfW0-
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Cao, Q., Shen, L., Xie, W., Parkhi, O.M., Zisserman, A.: Vggface2: A dataset for recognising faces across pose and age. In: 2018 13th IEEE International Conference on Automatic Face Gesture Recognition (FG 2018). pp. 67–74 (2018). https://doi.org/10.1109/FG.2018.00020
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Chattopadhay, A., Sarkar, A., Howlader, P., Balasubramanian, V.N.: Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). pp. 839–847 (2018). https://doi.org/10.1109/WACV.2018.00097
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Dhurandhar, A., Chen, P.Y., Luss, R., Tu, C.C., Ting, P., Shanmugam, K., Das, P.: Explanations based on the missing: Towards contrastive explanations with pertinent negatives. 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)
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Kim, B., Wattenberg, M., Gilmer, J., Cai, C., Wexler, J., Viegas, F., sayres, R.: Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (TCAV). In: Dy, J., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 80, pp. 2668–2677. PMLR (10–15 Jul 2018)
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Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. In: International Conference on Learning Representations (2018)
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Park, D.H., Hendricks, L.A., Akata, Z., Rohrbach, A., Schiele, B., Darrell, T., Rohrbach, M.: Multimodal explanations: Justifying decisions and pointing to the evidence. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2018)
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Petsiuk, V., Das, A., Saenko, K.: RISE: randomized input sampling for explanation of black-box models. In: British Machine Vision Conference 2018, BMVC 2018, Newcastle, UK, September 3-6, 2018. p. 151. BMVA Press (2018)
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Tan, S., Caruana, R., Hooker, G., Koch, P., Gordo, A.: Learning global additive explanations for neural nets using model distillation (2018)
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Wachter, S., Mittelstadt, B., Russell, C.: Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR. arvard Journal of Law and Technology, 31
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Zhang, Q., Wu, Y.N., Zhu, S.C.: Interpretable convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2018)
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Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2018)
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Zhao, Z., Dua, D., Singh, S.: Generating natural adversarial examples. In: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net (2018)
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Chen, C., Li, O., Tao, D., Barnett, A., Rudin, C., Su, J.K.: This looks like that: Deep learning for interpretable image recognition. 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)
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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)
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Goyal, Y., Wu, Z., Ernst, J., Batra, D., Parikh, D., Lee, S.: Counterfactual visual explanations. In: ICML. pp. 2376–2384 (2019)
2019
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Ignatiev, A., Narodytska, N., Marques-Silva, J.: On relating explanations and adversarial examples. 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)
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Cited alongside, same era.
2019
2021
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Jacob, P., Éloi Zablocki, Ben-Younes, H., Chen, M., Pérez, P., Cord, M.: Steex: Steering counterfactual explanations with semantics (2021)
2021
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Jalwana, M.A.A.K., Akhtar, N., Bennamoun, M., Mian, A.: Cameras: Enhanced resolution and sanity preserving class activation mapping for image saliency. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 16327–16336 (June 2021)
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Kong, Z., Ping, W.: On fast sampling of diffusion probabilistic models. In: ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models (2021)
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Cited alongside, same era.
Liu, S., Kailkhura, B., Loveland, D., Han, Y.: Generative counterfactual introspection for explainable deep learning. In: 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP). pp. 1–5 (2019)
2019
Cited alongside, same era.
Xian, Y., Sharma, S., Schiele, B., Akata, Z.: F-vaegan-d2: A feature generating framework for any-shot learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2019)
2019
Cited alongside, same era.
Akula, A., Wang, S., Zhu, S.C.: Cocox: Generating conceptual and counterfactual explanations via fault-lines. Proceedings of the AAAI Conference on Artificial Intelligence 34
2020
Cited alongside, same era.
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.F., Lin, H. (eds.) Advances in Neural Information Processing Systems. vol. 33, pp. 6840–6851. Curran Associates, Inc. (2020)
2020
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Huang, Z., Li, Y.: Interpretable and accurate fine-grained recognition via region grouping. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2020)
2020
Cited alongside, same era.
Mothilal, R.K., Sharma, A., Tan, C.: Explaining machine learning classifiers through diverse counterfactual explanations. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (2020)
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Poyiadzi, R., Sokol, K., Santos-Rodríguez, R., Bie, T.D., Flach, P.A.: Face: Feasible and actionable counterfactual explanations. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (2020)
2020
Cited alongside, same era.
Lee, J.R., Kim, S., Park, I., Eo, T., Hwang, D.: Relevance-cam: Your model already knows where to look. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 14944–14953 (June 2021)
2021
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Looveren, A.V., Klaise, J.: Interpretable counterfactual explanations guided by prototypes. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. pp. 650–665. Springer (2021)
2021
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Nauta, M., van Bree, R., Seifert, C.: Neural prototype trees for interpretable fine-grained image recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 14933–14943 (June 2021)
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Nichol, A.Q., Dhariwal, P.: Improved denoising diffusion probabilistic models (2021)
2021
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2021
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Petsiuk, V., Jain, R., Manjunatha, V., Morariu, V.I., Mehra, A., Ordonez, V., Saenko, K.: Black-box explanation of object detectors via saliency maps. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021, virtual, June 19-25, 2021. pp. 11443–11452. Computer Vision Foundation / IEEE (2021)
2021
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Rodríguez, P., Caccia, M., Lacoste, A., Zamparo, L., Laradji, I., Charlin, L., Vazquez, D.: Beyond trivial counterfactual explanations with diverse valuable explanations. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 1056–1065 (October 2021)
2021
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Saharia, C., Chan, W., Chang, H., Lee, C.A., Ho, J., Salimans, T., Fleet, D.J., Norouzi, M.: Palette: Image-to-image diffusion models. In: NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications (2021)
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2021
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Sauer, A., Geiger, A.: Counterfactual generative networks. In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net (2021)
2021
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Schut, L., Key, O., Mc Grath, R., Costabello, L., Sacaleanu, B., Corcoran, M., Gal, Y.: Generating interpretable counterfactual explanations by implicit minimisation of epistemic and aleatoric uncertainties. In: Banerjee, A., Fukumizu, K. (eds.) Proceedings of The 24th International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, vol. 130, pp. 1756–1764. PMLR (13–15 Apr 2021)
2021
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Shih, S.M., Tien, P.J., Karnin, Z.: GANMEX: One-vs-one attributions using GAN-based model explainability. In: Meila, M., Zhang, T. (eds.) Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event. Proceedings of Machine Learning Research, vol. 139, pp. 9592–9602. PMLR (2021)
2021
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Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. In: International Conference on Learning Representations (2021)
2021
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Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., Poole, B.: Score-based generative modeling through stochastic differential equations. In: International Conference on Learning Representations (2021)
2021
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Thiagarajan, J.J., Narayanaswamy, V., Rajan, D., Liang, J., Chaudhari, A., Spanias, A.: Designing counterfactual generators using deep model inversion. In: Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W. (eds.) Advances in Neural Information Processing Systems (2021), https://openreview.net/forum?id=iHisgL7PFj2
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2021
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Wang, P., Li, Y., Singh, K.K., Lu, J., Vasconcelos, N.: Imagine: Image synthesis by image-guided model inversion. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 3681–3690 (June 2021)
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Baranchuk, D., Voynov, A., Rubachev, I., Khrulkov, V., Babenko, A.: Label-efficient semantic segmentation with diffusion models. In: International Conference on Learning Representations (2022)
2022
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Meng, C., He, Y., Song, Y., Song, J., Wu, J., Zhu, J.Y., Ermon, S.: SDEdit: Guided image synthesis and editing with stochastic differential equations. In: International Conference on Learning Representations (2022)
2022
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