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We use concept-based interpretable models to mitigate shortcut learning.
The caltech-ucsd birds-200-2011 dataset
Wah, C., Branson, S., Welinder, P., Perona, P., and Belongie, S · 2011
Earlier work this paper cites.
Neural-symbolic learning and reasoning: contributions and challenges
Garcez, A. d., Besold, T. R., De Raedt, L., Földiak, P., Hitzler, P., Icard, T., Kühnberger, K.-U., Lamb, L. C., Miikkulainen, R., and Silver, D. L · 2015
Earlier work this paper cites.
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
” why should i trust you?” explaining the predictions of any classifier
Ribeiro, M. T., Singh, S., and Guestrin, C · 2016
Earlier work this paper cites.
Deep coral: Correlation alignment for deep domain adaptation, 2016
Sun, B. and Saenko, K · 2016
Earlier work this paper cites.
Neural-symbolic learning and reasoning: A survey and interpretation
Besold, T. R., Garcez, A. d., Bader, S., Bowman, H., Domingos, P., Hitzler, P., Kühnberger, K.-U., Lamb, L. C., Lowd, D., Lima, P. M. V., et al · 2017
Earlier work this paper cites.
Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q · 2017
Earlier work this paper cites.
Kim, B., Wattenberg, M., Gilmer, J., Cai, C., Wexler, J., Viegas, F., and Sayres, R · 2017
Earlier work this paper cites.
Seven-point checklist and skin lesion classification using multitask multimodal neural nets
Kawahara, J., Daneshvar, S., Argenziano, G., and Hamarneh, G · 2018
Earlier work this paper cites.
The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions
Tschandl, P., Rosendahl, C., and Kittler, H · 2018
Earlier work this paper cites.
Zero-shot learning—a comprehensive evaluation of the good, the bad and the ugly
Xian, Y., Lampert, C. H., Schiele, B., and Akata, Z · 2018
Earlier work this paper cites.
Selectivenet: A deep neural network with an integrated reject option
Geifman, Y. and El-Yaniv, R · 2019
Earlier work this paper cites.
Learning the difference that makes a difference with counterfactually-augmented data
Kaushik, D., Hovy, E., and Lipton, Z. C · 2019
Earlier work this paper cites.
Sagawa, S., Koh, P. W., Hashimoto, T. B., and Liang, P · 2019
Cited alongside, same era.
Entity, relation, and event extraction with contextualized span representations
Wadden, D., Wennberg, U., Luan, Y., and Hajishirzi, H · 2019
Cited alongside, same era.
Invariant risk minimization, 2020
Arjovsky, M., Bottou, L., Gulrajani, I., and Lopez-Paz, D · 2020
Cited alongside, same era.
Symbolic logic meets machine learning: A brief survey in infinite domains
Belle, V · 2020
Cited alongside, same era.
Debiasing skin lesion datasets and models? not so fast
Bissoto, A., Valle, E., and Avila, S · 2020
Cited alongside, same era.
Shortcut learning in deep neural networks
Metadata normalization
Lu, M., Zhao, Q., Zhang, J., Pohl, K. M., Fei-Fei, L., Niebles, J. C., and Adeli, E · 2021
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Patch shortcuts: Interpretable proxy models efficiently find black-box vulnerabilities
Rosenzweig, J., Sicking, J., Houben, S., Mock, M., and Akila, M · 2021
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A patient-centric dataset of images and metadata for identifying melanomas using clinical context
Rotemberg, V., Kurtansky, N., Betz-Stablein, B., Caffery, L., Chousakos, E., Codella, N., Combalia, M., Dusza, S., Guitera, P., Gutman, D., et al · 2021
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Gradient matching for domain generalization, 2021
Shi, Y., Seely, J., Torr, P. H. S., Siddharth, N., Hannun, A., Usunier, N., and Synnaeve, G · 2021
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Feature fusion vision transformer for fine-grained visual categorization
Wang, J., Yu, X., and Gao, Y · 2021
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Geirhos, R., Jacobsen, J.-H., Michaelis, C., Zemel, R., Brendel, W., Bethge, M., and Wichmann, F. A · 2020
Cited alongside, same era.
Concept bottleneck models
Koh, P. W., Nguyen, T., Tang, Y. S., Mussmann, S., Pierson, E., Kim, B., and Liang, P · 2020
Cited alongside, same era.
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., and Ahmed, S · 2020
Cited alongside, same era.
Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization, 2020
Sagawa, S., Koh, P. W., Hashimoto, T. B., and Liang, P · 2020
Cited alongside, same era.
Adversarial domain adaptation with domain mixup
Xu, M., Zhang, J., Ni, B., Li, T., Wang, C., Tian, Q., and Zhang, W · 2020
Cited alongside, same era.
Disparities in dermatology ai: Assessments using diverse clinical images
Daneshjou, R., Vodrahalli, K., Liang, W., Novoa, R. A., Jenkins, M., Rotemberg, V., Ko, J., Swetter, S. M., Bailey, E. E., Gevaert, O., et al · 2021
Cited alongside, same era.
Radgraph: Extracting clinical entities and relations from radiology reports
Jain, S., Agrawal, A., Saporta, A., Truong, S. Q., Duong, D. N., Bui, T., Chambon, P., Zhang, Y., Lungren, M. P., Ng, A. Y., et al · 2021
Cited alongside, same era.
Invariance principle meets information bottleneck for out-of-distribution generalization, 2022
Ahuja, K., Caballero, E., Zhang, D., Gagnon-Audet, J.-C., Bengio, Y., Mitliagkas, I., and Rish, I · 2022
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Entropy-based logic explanations of neural networks
Barbiero, P., Ciravegna, G., Giannini, F., Lió, P., Gori, M., and Melacci, S · 2022
Later among the works it cites.
Selective classification via neural network training dynamics
Rabanser, S., Thudi, A., Hamidieh, K., Dziedzic, A., and Papernot, N · 2022
Later among the works it cites.
Improving out-of-distribution robustness via selective augmentation
Yao, H., Wang, Y., Li, S., Zhang, L., Liang, W., Zou, J., and Finn, C · 2022
Later among the works it cites.
Anatomy-guided weakly-supervised abnormality localization in chest x-rays
Yu, K., Ghosh, S., Liu, Z., Deible, C., and Batmanghelich, K · 2022
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Post-hoc concept bottleneck models
Yuksekgonul, M., Wang, M., and Zou, J · 2022
Later among the works it cites.
Zarlenga, M. E., Barbiero, P., Ciravegna, G., Marra, G., Giannini, F., Diligenti, M., Shams, Z., Precioso, F., Melacci, S., Weller, A., et al · 2022
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Logic explained networks
Ciravegna, G., Barbiero, P., Giannini, F., Gori, M., Lió, P., Maggini, M., and Melacci, S · 2023
Closest in time.
Dividing and conquering a BlackBox to a mixture of interpretable models: Route, interpret, repeat
Ghosh, S., Yu, K., Arabshahi, F., and Batmanghelich, K · 2023
Closest in time.