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How Well do Feature Visualizations Support Causal Understanding of CNN Activations?
One widely used approach towards understanding the inner workings of dee...
06/23/2021 ∙ by Roland S. Zimmermann, et al. ∙
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Partial success in closing the gap between human and machine vision
A few years ago, the first CNN surpassed human performance on ImageNet. ...
06/14/2021 ∙ by Robert Geirhos, et al. ∙
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Exemplary Natural Images Explain CNN Activations Better than Feature Visualizations
Feature visualizations such as synthetic maximally activating images are...
10/23/2020 ∙ by Judy Borowski, et al. ∙
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On the surprising similarities between supervised and self-supervised models
How do humans learn to acquire a powerful, flexible and robust represent...
10/16/2020 ∙ by Robert Geirhos, et al. ∙
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Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency
A central problem in cognitive science and behavioural neuroscience as w...
06/30/2020 ∙ by Robert Geirhos, et al. ∙
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Shortcut Learning in Deep Neural Networks
Deep learning has triggered the current rise of artificial intelligence ...
04/16/2020 ∙ by Robert Geirhos, et al. ∙
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Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming
The ability to detect objects regardless of image distortions or weather...
07/17/2019 ∙ by Claudio Michaelis, et al. ∙
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ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Convolutional Neural Networks (CNNs) are commonly thought to recognise o...
11/29/2018 ∙ by Robert Geirhos, et al. ∙
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Generalisation in humans and deep neural networks
We compare the robustness of humans and current convolutional deep neura...
08/27/2018 ∙ by Robert Geirhos, et al. ∙
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Comparing deep neural networks against humans: object recognition when the signal gets weaker
Human visual object recognition is typically rapid and seemingly effortl...
06/21/2017 ∙ by Robert Geirhos, et al. ∙
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