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Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization., , , , , , и . ICLR, OpenReview.net, (2021)Methods and measurements to compare men against machines., , , , , , и . HVEI, стр. 36-45. Society for Imaging Science and Technology, (2017)Scaling Vision Transformers to 22 Billion Parameters., , , , , , , , , и 32 other автор(ы). ICML, том 202 из Proceedings of Machine Learning Research, стр. 7480-7512. PMLR, (2023)Getting aligned on representational alignment., , , , , , , , , и 19 other автор(ы). CoRR, (2023)Are Vision Language Models Texture or Shape Biased and Can We Steer Them?, , , , , , , и . CoRR, (2024)To err is human? A functional comparison of human and machine decision-making.. University of Tübingen, Germany, (2022)ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness., , , , , и . ICLR, OpenReview.net, (2019)Trivial or Impossible --- dichotomous data difficulty masks model differences (on ImageNet and beyond)., , , и . ICLR, OpenReview.net, (2022)Towards flexible perception with visual memory., , , , , , , и . CoRR, (2024)ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness., , , , , и . CoRR, (2018)