Fetching the paper…
Reading the bibliography…
A dataset is confounded if it is most easily solved via a spurious correlation, which fails to generalize to new data.
Catastrophic interference in connectionist networks: The sequential learning problem
McCloskey, M. and Cohen, N. J · 1989
Earlier work this paper cites.
Handwritten Character Recognition Using Neural Network Architectures
Matan, O., Kiang, R., Stenard, C. E., Boser, B. E., Denker, J., Henderson, D., Hubbard, W., Jackel, L., and LeCun, Y · 1990
Earlier work this paper cites.
Connectionist Models of Recognition Memory: Constraints Imposed by Learning and Forgetting Functions
Ratcliff, R · 1990
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Earlier work this paper cites.
An empirical investigation of catastrophic forgetting in gradient-based neural networks
Goodfellow, I. J., Mirza, M., Xiao, D., Courville, A., and Bengio, Y · 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.
Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
Johnson, J., Hariharan, B., van der Maaten, L., Fei-Fei, L., Zitnick, C. L., and Girshick, R · 2017
Earlier work this paper cites.
Overcoming catastrophic forgetting in neural networks
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., and et al · 2017
Earlier work this paper cites.
Right for the right reasons: Training differentiable models by constraining their explanations
Ross, A. S., Hughes, M. C., and Doshi-Velez, F · 2017
Earlier work this paper cites.
Lifelong Machine Learning
Chen, Z., Liu, B., Brachman, R., Stone, P., and Rossi, F · 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.
On tiny episodic memories in continual learning
Chaudhry, A., Rohrbach, M., Elhoseiny, M., Ajanthan, T., Dokania, P. K., Torr, P. H. S., and Ranzato, M · 2019
Earlier work this paper cites.
Codella, N., Rotemberg, V., Tschandl, P., Celebi, M. E., Dusza, S., Gutman, D., Helba, B., Kalloo, A., Liopyris, K., Marchetti, M., et al · 2019
Earlier work this paper cites.
Learning not to learn: Training deep neural networks with biased data
Kim, B., Kim, H., Kim, K., Kim, S., and Kim, J · 2019
Earlier work this paper cites.
Unmasking clever hans predictors and assessing what machines really learn
Lapuschkin, S., Wäldchen, S., Binder, A., Montavon, G., Samek, W., and Müller, K.-R · 2019
Earlier work this paper cites.
Set transformer: A framework for attention-based permutation-invariant neural networks
Lee, J., Lee, Y., Kim, J., Kosiorek, A. R., Choi, S., and Teh, Y. W · 2019
Cited alongside, same era.
Continual lifelong learning with neural networks: A review
Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., and Wermter, S · 2019
Cited alongside, same era.
Moment matching for multi-source domain adaptation
Peng, X., Bai, Q., Xia, X., Huang, Z., Saenko, K., and Wang, B · 2019
Cited alongside, same era.
Continual lifelong learning in natural language processing: A survey
Biesialska, M., Biesialska, K., and Costa-jussà, M. R · 2020
Cited alongside, same era.
Dark experience for general continual learning: a strong, simple baseline
Buzzega, P., Boschini, M., Porrello, A., Abati, D., and Calderara, S · 2020
Cited alongside, same era.
Shortcut learning in deep neural networks
Geirhos, R., Jacobsen, J.-H., Michaelis, C., Zemel, R., Brendel, W., Bethge, M., and Wichmann, F. A · 2020
WILDS: A benchmark of in-the-wild distribution shifts
Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., Lee, T., David, E., Stavness, I., Guo, W., Earnshaw, B. A., Haque, I. S., Beery, S., Leskovec, J., Kundaje, A., Pierson, E., Levine, S., Finn, C., and Liang, P · 2021
Later among the works it cites.
Right for the right concept: Revising neuro-symbolic concepts by interacting with their explanations
Stammer, W., Schramowski, P., and Kersting, K · 2021
Later among the works it cites.
A continual learning survey: Defying forgetting in classification tasks
De Lange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., and Tuytelaars, T · 2022
Later among the works it cites.
Biological underpinnings for lifelong learning machines
Kudithipudi, D., Aguilar-Simon, M., Babb, J., Bazhenov, M., Blackiston, D., Bongard, J., Brna, A. P., Chakravarthi Raja, S., Cheney, N., Clune, J., et al · 2022
Later among the works it cites.
CLEVA-compass: A continual learning evaluation assessment compass to promote research transparency and comparability
Mundt, M., Lang, S., Delfosse, Q., and Kersting, K · 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Object-centric learning with slot attention
Locatello, F., Weissenborn, D., Unterthiner, T., Mahendran, A., Heigold, G., Uszkoreit, J., Dosovitskiy, A., and Kipf, T · 2020
Cited alongside, same era.
Continual deep learning by functional regularisation of memorable past
Pan, P., Swaroop, S., Immer, A., Eschenhagen, R., Turner, R., and Khan, M. E. E · 2020
Cited alongside, same era.
Gdumb: A simple approach that questions our progress in continual learning
Prabhu, A., Torr, P., and Dokania, P · 2020
Cited alongside, same era.
Interpretations are useful: penalizing explanations to align neural networks with prior knowledge
Rieger, L., Singh, C., Murdoch, W., and Yu, B · 2020
Cited alongside, same era.
Distributionally robust neural networks
Sagawa, S., Koh, P. W., Hashimoto, T. B., and Liang, P · 2020
Cited alongside, same era.
Making deep neural networks right for the right scientific reasons by interacting with their explanations
Schramowski, P., Stammer, W., Teso, S., Brugger, A., Herbert, F., Shao, X., Luigs, H., Mahlein, A., and Kersting, K · 2020
Cited alongside, same era.
Later among the works it cites.
Rusu, A. A., Rabinowitz, N. C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., and Hadsell, R · 2022
Later among the works it cites.
Concept-level debugging of part-prototype networks
Bontempelli, A., Teso, S., Tentori, K., Giunchiglia, F., and Passerini, A · 2023
Later among the works it cites.
Towards causal replay for knowledge rehearsal in continual learning
Churamani, N., Cheong, J., Kalkan, S., and Gunes, H · 2023
Later among the works it cites.
A typology for exploring the mitigation of shortcut behaviour
Friedrich, F., Stammer, W., Schramowski, P., and Kersting, K · 2023
Later among the works it cites.
Issues for continual learning in the presence of dataset bias
Lee, D., Jung, S., and Moon, T · 2023
Later among the works it cites.
Neuro-symbolic continual learning: Knowledge, reasoning shortcuts and concept rehearsal
Marconato, E., Bontempo, G., Ficarra, E., Calderara, S., Passerini, A., and Teso, S · 2023
Later among the works it cites.
Causal fairness under unobserved confounding: A neural sensitivity framework
Schröder, M., Frauen, D., and Feuerriegel, S · 2023
Later among the works it cites.
Discover and cure: Concept-aware mitigation of spurious correlation
Wu, S., Yuksekgonul, M., Zhang, L., and Zou, J · 2023
Later among the works it cites.
Croissant: A metadata format for ml-ready datasets
Akhtar, M., Benjelloun, O., Conforti, C., Gijsbers, P., Giner-Miguelez, J., Jain, N., Kuchnik, M., Lhoest, Q., Marcenac, P., Maskey, M., Mattson, P., Oala, L., Ruyssen, P., Shinde, R., Simperl, E., Thomas, G., Tykhonov, S., Vanschoren, J., van der Velde, J., Vogler, S., and Wu, C.-J · 2024
Closest in time.
Continual learning in the presence of spurious correlations: Analyses and a simple baseline
Lee, D., Jung, S., and Moon, T · 2024
Closest in time.
Steinmann, D., Divo, F., Kraus, M., Wüst, A., Struppek, L., Friedrich, F., and Kersting, K · 2024
Closest in time.
Spurious correlations in machine learning: A survey
Ye, W., Zheng, G., Cao, X., Ma, Y., and Zhang, A · 2024
Closest in time.