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- W4320724748 abstract "There are several problems with the robustness of Convolutional Neural Networks (CNNs). For example, the prediction of CNNs can be changed by adding a small magnitude of noise to an input, and the performances of CNNs are degraded when the distribution of input is shifted by a transformation never seen during training (e.g., the blur effect). There are approaches to replace pixel values with binary embeddings to tackle the problem of adversarial perturbations, which successfully improve robustness. In this work, we propose Pixel to Binary Embedding (P2BE) to improve the robustness of CNNs. P2BE is a learnable binary embedding method as opposed to previous hand-coded binary embedding methods. P2BE outperforms other binary embedding methods in robustness against adversarial perturbations and visual corruptions that are not shown during training." @default.
- W4320724748 created "2023-02-15" @default.
- W4320724748 creator A5042739835 @default.
- W4320724748 creator A5051635592 @default.
- W4320724748 date "2022-06-13" @default.
- W4320724748 modified "2023-09-29" @default.
- W4320724748 title "Pixel to Binary Embedding Towards Robustness for CNNs" @default.
- W4320724748 doi "https://doi.org/10.48550/arxiv.2206.05898" @default.
- W4320724748 hasPublicationYear "2022" @default.
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