Generalisation in humans and deep neural networks

NeurIPS 2018 Robert GeirhosCarlos R. Medina TemmeJonas RauberHeiko H. SchüttMatthias BethgeFelix A. Wichmann

We compare the robustness of humans and current convolutional deep neural networks (DNNs) on object recognition under twelve different types of image degradations. First, using three well known DNNs (ResNet-152, VGG-19, GoogLeNet) we find the human visual system to be more robust to nearly all of the tested image manipulations, and we observe progressively diverging classification error-patterns between humans and DNNs when the signal gets weaker... (read more)

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