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A major challenge to out-of-distribution generalization is reliance on spurious features -- patterns that are predictive of the class label in the training data distribution, but not causally related to the target.
Survey Sampling
Kish, L · 1965
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Learning Multiple Layers Of Features From Tiny Images
Krizhevsky, A. and Hinton, G · 2009
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Learning Multiple Layers Of Features From Tiny Images
Krizhevsky, A., Hinton, G., et al · 2009
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Deep Learning Face Attributes In The Wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
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ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al · 2015
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Deep Residual Learning For Image Recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Focal loss for dense object detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Dollár, P · 2017
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Chestx-ray8: Hospital-scale Chest X-Ray Database And Benchmarks On Weakly-Supervised Classification And Localization Of Common Thorax Diseases
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., and Summers, R. M · 2017
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A Broad-Coverage Challenge Corpus For Sentence Understanding Through Inference
Williams, A., Nangia, N., and Bowman, S. R · 2017
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BERT: Pre-training Of Deep Bidirectional Transformers For Language Understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
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Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F. A., and Brendel, W · 2018
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Variable Generalization Performance Of A Deep Learning Model To Detect Pneumonia In Chest Radiographs: A Cross-Sectional Study
Zech, J. R., Badgeley, M. A., Liu, M., Costa, A. B., Titano, J. J., and Oermann, E. K · 2018
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Nuanced Metrics For Measuring Unintended Bias With Real Data For Text Classification
Borkan, D., Dixon, L., Sorensen, J., Thain, N., and Vasserman, L · 2019
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Approximating CNNs With Bag-of-local-Features Models Works Surprisingly Well On ImageNet
Brendel, W. and Bethge, M · 2019
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Class-balanced Loss Based On Effective Number Of Samples
Cui, Y., Jia, M., Lin, T.-Y., Song, Y., and Belongie, S · 2019
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CheXpert: A Large Chest Radiograph Dataset With Uncertainty Labels And Expert Comparison
Irvin, J., Rajpurkar, P., Ko, M., Yu, Y., Ciurea-Ilcus, S., Chute, C., Henrik Marklund, B. H., Ball, R., Shpanskaya, K., Seekins, J., Mong, D. A., Halabi, S. S., Sandberg, J. K., Jones, R., Larson, D. B., Langlotz, C. P., Patel, B. N., Lungren, M. P., and Ng, A. Y · 2019
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Shortcut Learning In Deep Neural Networks
Geirhos, R., Jacobsen, J.-H., Michaelis, C., Zemel, R., Brendel, W., Bethge, M., and Wichmann, F. A · 2020
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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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Just Train Twice: Improving Group Robustness Without Training Group Information
Liu, E. Z., Haghgoo, B., Chen, A. S., Raghunathan, A., Koh, P. W., Sagawa, S., Liang, P., and Finn, C · 2021
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BARACK: Partially Supervised Group Robustness With Guarantees
Sohoni, N., Sanjabi, M., Ballas, N., Grover, A., Nie, S., Firooz, H., and Ré, C · 2021
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Robust Representation Learning Via Perceptual Similarity Metrics
Taghanaki, S. A., Choi, K., Khasahmadi, A., and Goyal, A · 2021
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On Feature Learning in the Presence of Spurious Correlations
Izmailov, P., Kirichenko, P., Gruver, N., and Wilson, A. G · 2022
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Overparameterisation and Worst-Case Generalisation: Friend or Foe?
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Learning From Failure: De-biasing Classifier From Biased Classifier
Nam, J., Cha, H., Ahn, S., Lee, J., and Shin, J · 2020
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Hidden Stratification Causes Clinically Meaningful Failures In Machine Learning For Medical Imaging
Oakden-Rayner, L., Dunnmon, J., Carneiro, G., and Ré, C · 2020
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Distributionally Robust Neural Networks For Group Shifts: On The Importance Of Regularization For Worst-Case Generalization
Sagawa, S., Koh, P. W., Hashimoto, T. B., and Liang, P · 2020
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No Subclass Left Behind: Fine-Grained Robustness In Coarse-Grained Classification Problems
Sohoni, N., Dunnmon, J., Angus, G., Gu, A., and Ré, C · 2020
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Noise Or Signal: The Role Of Image Backgrounds In Object Recognition
Xiao, K., Engstrom, L., Ilyas, A., and Madry, A · 2020
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Simple Data Balancing Achieves Competitive Worst-Group-Accuracy
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Last Layer Re-Training Is Sufficient For Robustness To Spurious Correlations
Kirichenko, P., Izmailov, P., and Wilson, A. G · 2022
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ZIN: When and How to Learn Invariance Without Environment Partition?
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Moayeri, M., Pope, P., Balaji, Y., and Feizi, S · 2022
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Spread Spurious Attribute: Improving Worst-group Accuracy With Spurious Attribute Estimation
Nam, J., Kim, J., Lee, J., and Shin, J · 2022
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Chroma-VAE: Mitigating Shortcut Learning With Generative Classifiers
Yang, W., Kirichenko, P., Goldblum, M., and Wilson, A. G · 2022
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Correct-n-Contrast: A Contrastive Approach For Improving Robustness To Spurious Correlations
Zhang, M., Sohoni, N. S., Zhang, H. R., Finn, C., and Ré, C · 2022
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Change is Hard: A Closer Look at Subpopulation Shift
Yang, Y., Zhang, H., Katabi, D., and Ghassemi, M · 2023
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