Titlebook: Computer Vision – ACCV 2022 Workshops; 16th Asian Conferenc Yinqiang Zheng,Hacer Yalim Kele?,Piotr Koniusz Conference proceedings 2023 The

Grating

On the Ring-LWE and Polynomial-LWE Problemsclass. This adversarial pattern is universal for the class, which means that it can mislead the DNN model on all input images of the class with high probability. We demonstrate our idea on MNIST dataset, and the results show that ADVFilter can achieve up to 90. success rate with only 16 correspondin

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Overdrive: Making SPDZ Great Againni-FL first performs unsupervised learning for the gradients received to define the grouping policy. Then, the server divides the gradients received into different groups according to the grouping policy defined and performs byzantine-robust aggregation. Finally, the server calculates the weighted m

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系列

DEBT

Marcel Keller,Valerio Pastro,Dragos Rotarurom the target domain along with labeled source samples are used to adapt the detector using an over-fitting aware and periodic gradient update based joint few-shot fine-tuning technique. Further, we utilize a self-supervision scheme to obtain pseudo-labels having high-confidence on the unlabeled ta

遷移

Pavel Hubá?ek,Alon Rosen,Margarita Valdh a texture that is statistically consistent with the surrounding skin. To achieve this, we introduce a novel loss term that reuses the wrinkle segmentation network to penalize those regions that still contain wrinkles after the inpainting. We evaluate our method qualitatively and quantitatively, sh

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Scleroderma

accomplishment

Vincent Grosso,Fran?ois-Xavier Standaertfeature hallucination, and aims to construct a practical model with small size and high efficiency for real imaging systems. Specifically, we exploit a deep feature hallucination module (DFHM) for duplicating more features with cheap operations as the main component, and stack multiple of them to co

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