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Models trained on one set of domains often suffer performance drops on unseen domains, e.g., when wildlife monitoring models are deployed in new camera locations.
Quantification of histochemical staining by color deconvolution
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A distribution-free theory of nonparametric regression , volume 1
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An analysis of random design linear regression
Hsu, D., Kakade, S. M., and Zhang, T · 2011
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A short note on the tail bound of wishart distribution
Zhu, S · 2012
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Domain-adversarial training of neural networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., and Lempitsky, V · 2016
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Deep coral: Correlation alignment for deep domain adaptation
Sun, B. and Saenko, K · 2016
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Improved regularization of convolutional neural networks with cutout
DeVries, T. and Taylor, G. W · 2017
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Correlation alignment for unsupervised domain adaptation
Sun, B., Feng, J., and Saenko, K · 2017
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mixup: Beyond empirical risk minimization
Zhang, H., Cisse, M., Dauphin, Y. N., and Lopez-Paz, D · 2017
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From detection of individual metastases to classification of lymph node status at the patient level: the camelyon17 challenge
Bandi, P., Geessink, O., Manson, Q., Van Dijk, M., Balkenhol, M., Hermsen, M., Bejnordi, B. E., Lee, B., Paeng, K., Zhong, A., et al · 2018
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Recognition in terra incognita
Beery, S., Van Horn, G., and Perona, P · 2018
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Cycada: Cycle-consistent adversarial domain adaptation
Hoffman, J., Tzeng, E., Park, T., Zhu, J.-Y., Isola, P., Saenko, K., Efros, A., and Darrell, T · 2018
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Conditional adversarial domain adaptation
Long, M., Cao, Z., Wang, J., and Jordan, M. I · 2018
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Whole-slide mitosis detection in h&e breast histology using phh3 as a reference to train distilled stain-invariant convolutional networks
Tellez, D., Balkenhol, M., Otte-Höller, I., van de Loo, R., Vogels, R., Bult, P., Wauters, C., Vreuls, W., Mol, S., Karssemeijer, N., et al · 2018
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Generalizing to unseen domains via distribution matching
Albuquerque, I., Monteiro, J., Darvishi, M., Falk, T. H., and Mitliagkas, I · 2019
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Arjovsky, M., Bottou, L., Gulrajani, I., and Lopez-Paz, D · 2019
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Efficient pipeline for camera trap image review
Beery, S., Morris, D., and Yang, S · 2019
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A kernel theory of modern data augmentation
Dao, T., Gu, A., Ratner, A., Smith, V., De Sa, C., and Ré, C · 2019
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Data augmentation revisited: Rethinking the distribution gap between clean and augmented data
He, Z., Xie, L., Chen, X., Zhang, Y., Wang, Y., and Tian, Q · 2019
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Specaugment: A simple data augmentation method for automatic speech recognition
Park, D. S., Chan, W., Zhang, Y., Chiu, C.-C., Zoph, B., Cubuk, E. D., and Le, Q. V · 2019
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Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology
Tellez, D., Litjens, G., Bándi, P., Bulten, W., Bokhorst, J.-M., Ciompi, F., and van der Laak, J · 2019
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Cutmix: Regularization strategy to train strong classifiers with localizable features
Yun, S., Han, D., Oh, S. J., Chun, S., Choe, J., and Yoo, Y · 2019
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Synthetic examples improve generalization for rare classes
Beery, S., Liu, Y., Morris, D., Piavis, J., Kapoor, A., Joshi, N., Meister, M., and Perona, P · 2020
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A group-theoretic framework for data augmentation
Chen, S., Dobriban, E., and Lee, J. H · 2020
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Information-theoretic generalization bounds for meta-learning and applications
Jose, S. T. and Simeone, O · 2021
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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., et al · 2021
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Domain generalization using causal matching
Mahajan, D., Tople, S., and Sharma, A · 2021
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Accuracy on the line: on the strong correlation between out-of-distribution and in-distribution generalization
Miller, J. P., Taori, R., Raghunathan, A., Sagawa, S., Koh, P. W., Shankar, V., Liang, P., Carmon, Y., and Schmidt, L · 2021
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Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
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Randaugment: Practical automated data augmentation with a reduced search space
Cubuk, E. D., Zoph, B., Shlens, J., and Le, Q. V · 2020
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Underspecification presents challenges for credibility in modern machine learning
D’Amour, A., Heller, K., Moldovan, D., Adlam, B., Alipanahi, B., Beutel, A., Chen, C., Deaton, J., Eisenstein, J., Hoffman, M. D., et al · 2020
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Affinity and diversity: Quantifying mechanisms of data augmentation
Gontijo-Lopes, R., Smullin, S. J., Cubuk, E. D., and Dyer, E · 2020
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In search of lost domain generalization
Gulrajani, I. and Lopez-Paz, D · 2020
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On the benefits of invariance in neural networks
Lyle, C., van der Wilk, M., Kwiatkowska, M., Gal, Y., and Bloem-Reddy, B · 2020
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The risks of invariant risk minimization
Rosenfeld, E., Ravikumar, P., and Risteski, A · 2020
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Heterogeneous domain generalization via domain mixup
Wang, Y., Li, H., and Kot, A. C · 2020
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Robey, A., Pappas, G. J., and Hassani, H · 2021
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Extending the wilds benchmark for unsupervised adaptation
Sagawa, S., Koh, P. W., Lee, T., Gao, I., Xie, S. M., Shen, K., Kumar, A., Hu, W., Yasunaga, M., Marklund, H., et al · 2021
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A fine-grained analysis on distribution shift
Wiles, O., Gowal, S., Stimberg, F., Rebuffi, S.-A., Ktena, I., Dvijotham, K. D., and Cemgil, A. T · 2021
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Improving bird classification with unsupervised sound separation
Denton, T., Wisdom, S., and Hershey, J. R · 2022
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A collection of fully-annotated soundscape recordings from the Southwestern Amazon Basin, September 2022
Hopping, W. A., Kahl, S., and Klinck, H · 2022
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A collection of fully-annotated soundscape recordings from the Northeastern United States, August 2022
Kahl, S., Charif, R., and Klinck, H · 2022
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How to fine-tune vision models with sgd
Kumar, A., Shen, R., Bubeck, S., and Gunasekar, S · 2022
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A collection of fully-annotated soundscape recordings from the Island of Hawai’i, September 2022
Navine, A., Kahl, S., Tanimoto-Johnson, A., Klinck, H., and Hart, P · 2022
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Nuisances via negativa: Adjusting for spurious correlations via data augmentation
Puli, A., Joshi, N., He, H., and Ranganath, R · 2022
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Noise reduction in python using spectral gating, 2022
Sainburg, T · 2022
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Out-of-distribution generalization with causal invariant transformations
Wang, R., Yi, M., Chen, Z., and Zhu, S · 2022
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Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Wortsman, M., Ilharco, G., Gadre, S. Y., Roelofs, R., Gontijo-Lopes, R., Morcos, A. S., Namkoong, H., Farhadi, A., Carmon, Y., Kornblith, S., et al · 2022
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Improving out-of-distribution robustness via selective augmentation
Yao, H., Wang, Y., Li, S., Zhang, L., Liang, W., Zou, J., and Finn, C · 2022
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