[44] Z. Li and N. Snavely. Cgintrinics: Better intrinsic image decomposition through physically-based rendering. In European Conference on Computer Vision (ECCV), 2018.
[45] P. Linardatos, V. Papastefanopoulos, and S. Kotsiantis. Explainable ai: A review of machine learning interpretability methods. Entropy, 23(1):18, 2020.
[46] Y. Ma, X. Feng, X. Jiang, Z. Xia, and J. Peng. Intrinsic image decomposition: A comprehensive review. In Image and Graphics: 9th International Conference, ICIG 2017, Shanghai, China, September 13-15, 2017, Revised Selected Papers, Part I 9, pages 626-638. Springer, 2017.
[47] M. Moayeri, K. Rezaei, M. Sanjabi, and S. Feizi. Text-to-concept (and back) via cross-model alignment. arXiv preprint arXiv:2305.06386, 2023.
[48] I. Nassar, M. Hayat, E. Abbasnejad, H. Rezatofighi, and G. Haffari. Protocon: Pseudo-label refinement via online clustering and prototypical consistency for efficient semi-supervised learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11641-11650, 2023.
[49] C. Niu, H. Shan, and G. Wang. Spice: Semantic pseudo-labeling for image clustering. IEEE Transactions on Image Processing, 31:7264-7278, 2022.
[50] A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PMLR, 2021.
[51] L. Rieger, C. Singh, W. Murdoch, and B. Yu. Interpretations are useful: penalizing explanations to align neural networks with prior knowledge. In International conference on machine learning, pages 8116-8126. PMLR, 2020.
[52] A. S. Ross, M. C. Hughes, and F. Doshi-Velez. Right for the right reasons: Training differentiable models by constraining their explanations. arXiv preprint arXiv:1703.03717, 2017.
[53] G. Saha and K. Roy. Saliency guided experience packing for replay in continual learning. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 5273-5283, 2023.
[54] W. Samek, T. Wiegand, and K.-R. Müller. Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. arXiv preprint arXiv:1708.08296, 2017.
[55] Y. Sawada and K. Nakamura. C-senn: Contrastive self-explaining neural network. ArXiv, abs/2206.09575, 2022.
[56] Y. Sawada and K. Nakamura. Concept bottleneck model with additional unsupervised concepts. IEEE Access, 10:41758-41765, 2022.
[57] J. Schrouff, S. Baur, S. Hou, D. Mincu, E. Loreaux, R. Blanes, J. Wexler, A. Karthikesalingam, and B. Kim. Best of both worlds: local and global explanations with human-understandable concepts. arXiv preprint arXiv:2106.08641, 2021.
[58] G. Schwalbe. Concept embedding analysis: A review. arXiv preprint arXiv:2203.13909, 2022.
[59] R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE international conference on computer vision, pages 618-626, 2017.
[60] D. Smilkov, N. Thorat, B. Kim, F. Viégas, and M. Wattenberg. Smoothgrad: removing noise by adding noise. arXiv preprint arXiv:1706.03825, 2017.
[61] Y. Song, S. K. Shyn, and K.-s. Kim. Img2tab: Automatic class relevant concept discovery from stylegan features for explainable image classification. arXiv preprint arXiv:2301.06324, 2023.
[62] R. Soni, N. Shah, C. T. Seng, and J. D. Moore. Adversarial tcav - robust and effective interpretation of intermediate layers in neural networks. ArXiv, abs/2002.03549, 2020.
[63] K. Tanwisuth, X. Fan, H. Zheng, S. Zhang, H. Zhang, B. Chen, and M. Zhou. A prototype-oriented framework for unsupervised domain adaptation. Advances in Neural Information Processing Systems, 34: 17194-17208, 2021.
[64] E. Tartaglione, C. A. Barbano, and M. Grangetto. End: Entangling and disentangling deep representations for bias correction. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 13508-13517, 2021.