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Trivial or Impossible --- dichotomous data difficulty masks model differences (on ImageNet and beyond).

, , , and . ICLR, OpenReview.net, (2022)

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