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- W3199491006 abstract "The ability to detect edges is a fundamental attribute necessary to truly capture visual concepts. In this paper, we show that edges cannot be represented properly in the first layer of a conventional convolutional neural network, and further show that they are poorly captured in two popular neural network architectures VGG-16 and ResNet. The neural networks are found to rely on color information, which might vary in unexpected ways outside of the datasets used for their evaluation. To improve their robustness, we propose edge-detection units and show that they reduce performance loss and generate qualitatively different representations. By comparing various models, we show that the robustness of edge detection is an important factor contributing to the robustness of models against color noise." @default.
- W3199491006 created "2021-09-27" @default.
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- W3199491006 date "2021-07-18" @default.
- W3199491006 modified "2023-09-27" @default.
- W3199491006 title "Revisiting Edge Detection in Convolutional Neural Networks" @default.
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- W3199491006 doi "https://doi.org/10.1109/ijcnn52387.2021.9534039" @default.
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