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Machine learning models that incorporate concept learning as an intermediate step in their decision making process can match the performance of black-box predictive models while retaining the ability to explain outcomes in human understandable terms.
Attribute and simile classifiers for face verification
Kumar, N., Berg, A. C., Belhumeur, P. N., and Nayar, S. K · 2009
Earlier work this paper cites.
Learning to detect unseen object classes by between-class attribute transfer
Lampert, C. H., Nickisch, H., and Harmeling, S · 2009
Earlier work this paper cites.
Semantic bottleneck for computer vision tasks
Bucher, M., Herbin, S., and Jurie, F · 2018
Earlier work this paper cites.
Clinically applicable deep learning for diagnosis and referral in retinal disease
De Fauw, J., Ledsam, J. R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., Askham, H., Glorot, X., O’Donoghue, B., Visentin, D., et al · 2018
Earlier work this paper cites.
Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)
Kim, B., Wattenberg, M., Gilmer, J., Cai, C., Wexler, J., Viegas, F., et al · 2018
Earlier work this paper cites.
Learning latent subspaces in variational autoencoders
Klys, J., Snell, J., and Zemel, R · 2018
Cited alongside, same era.
Neural-symbolic vqa: Disentangling reasoning from vision and language understanding
Yi, K., Wu, J., Gan, C., Torralba, A., Kohli, P., and Tenenbaum, J. B · 2018
Cited alongside, same era.
Interpreting deep visual representations via network dissection
Zhou, B., Bau, D., Oliva, A., and Torralba, A · 2018
Cited alongside, same era.
Towards automatic concept-based explanations
Ghorbani, A., Wexler, J., Zou, J., and Kim, B · 2019
Cited alongside, same era.
Interpretability beyond classification output: Semantic bottleneck networks
Losch, M., Fritz, M., and Schiele, B · 2019
Later among the works it cites.
Concept whitening for interpretable image recognition
Chen, Z., Bei, Y., and Rudin, C · 2020
Later among the works it cites.
Concept bottleneck models
Koh, P. W., Nguyen, T., Tang, Y. S., Mussmann, S., Pierson, E., Kim, B., and Liang, P · 2020
Later among the works it cites.
Do concept bottleneck models learn as intended?
Margeloiu, A., Ashman, M., Bhatt, U., Chen, Y., Jamnik, M., and Weller, A · 2021
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