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Robust deep learning object recognition models rely on low frequency information in natural images., , , , , , , , and . PLoS Comput. Biol., (March 2023)Adapting ImageNet-scale models to complex distribution shifts with self-learning., , , , , and . CoRR, (2021)Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming., , , , , , , and . CoRR, (2019)Increasing the robustness of DNNs against image corruptions by playing the Game of Noise., , , , , , and . CoRR, (2020)If your data distribution shifts, use self-learning., , , , , , , and . Trans. Mach. Learn. Res., (2022)Effective pruning of web-scale datasets based on complexity of concept clusters., , , , , and . CoRR, (2024)Does CLIP's Generalization Performance Mainly Stem from High Train-Test Similarity?, , , , and . CoRR, (2023)Improving robustness against common corruptions by covariate shift adaptation., , , , , and . NeurIPS, (2020)A Simple Way to Make Neural Networks Robust Against Diverse Image Corruptions., , , , , , and . ECCV (3), volume 12348 of Lecture Notes in Computer Science, page 53-69. Springer, (2020)