Hans Pinckaers — Public notes

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Papers on Concept Activation Vectors

hanspinckaers updated 8 days ago created: 16 February 2021
Deep learning Public


On completeness-aware concept-based explanations in deep neural networks. Yeh, C.-K., Kim, B., Arik, S., Li, C.-L., Ravikumar, P., & Pfister, T. (2020). https://arxiv.org/abs/1910.07969

Debugging Tests for Model Explanations. Adebayo, J., Muelly, M., Liccardi, I., & Kim, B. (2020). arXiv, 2011.05429v1. http://arxiv.org/abs/2011.05429v1

Concept Whitening for Interpretable Image Recognition. Chen, Z., Bei, Y., & Rudin, C. (2020). Nature Machine Intelligence, Vol 2, Dec 2020, 772-782, 2002.01650v5. http://arxiv.org/abs/2002.01650v5

Concept Bottleneck Models. Koh, P. W., Nguyen, T., Tang, Y. S., Mussmann, S., Pierson, E., Kim, B., & Liang, P. (2020). arXiv, 2007.04612v3. http://arxiv.org/abs/2007.04612v3

Towards Automatic Concept-based Explanations. Ghorbani, A., Wexler, J., Zou, J., & Kim, B. (2019). arXiv, 1902.03129v3. http://arxiv.org/abs/1902.03129v3

Explaining Classifiers with Causal Concept Effect (CaCE). Goyal, Y., Feder, A., Shalit, U., & Kim, B. (2019). arXiv, 1907.07165v2. http://arxiv.org/abs/1907.07165v2

Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (TCAV). Kim, B., Wattenberg, M., Gilmer, J., Cai, C., Wexler, J., & Viegas, F. (2018). In International conference on machine learning (pp. 2668-2677). PMLR. http://proceedings.mlr.press/v80/kim18d.html

Application (medical domain)

Robust and Interpretable Convolutional Neural Networks to Detect Glaucoma in Optical Coherence Tomography Images. Thakoor, K. A., Koorathota, S. C., Hood, D. C., & Sajda, P. (2020). IEEE Trans Biomed Eng, PP. https://doi.org/10.1109/TBME.2020.3043215

On interpretability of deep learning based skin lesion classifiers using concept activation vectors. Lucieri, A., Bajwa, M. N., Braun, S. A., Malik, M. I., Dengel, A., & Ahmed, S. (2020). International Joint Conference on Neural Networks (IJCNN) (pp. 1-10). IEEE. https://ieeexplore.ieee.org/document/9206946

Concept-based model explanations for Electronic Health Records. Baur, S., Hou, S., Loreaux, E., Mincu, D., Mottram, A., Protsyuk, I., Tomasev, N., Seneviratne, M. G., Karthikesanlingam, A., & Schrouff, J. (2020). arXiv, 2012.02308v1. http://arxiv.org/abs/2012.02308v1