西瓜
发表于 2025-3-30 09:29:11
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deface
发表于 2025-3-30 13:10:24
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Bereavement
发表于 2025-3-30 16:42:13
Alexander Romanovskyensive experiments on two lane detection benchmark datasets show that our method could achieve the state-of-the-art performance in terms of both speed and accuracy. A light weight version could even achieve 300+ frames per second with the same resolution, which is at least 4x faster than previous st
Flounder
发表于 2025-3-30 23:53:29
Rainer Gmehlich,Cliff Jonestimization which is pervasively adopted in DNN models. We evaluate the efficacy of . for generating targeted poisoning attacks via extensive experiments using various datasets and DNN models. Results show that . is effective with a rather high success rate while preserving all the claimed properties
神圣将军
发表于 2025-3-31 01:15:03
Jérôme Falampin,Hung Le-Dang,Michael Leuschel,Mikael Mokrani,Daniel Plaggeraining data. Our method is developed without extra-dependency, thus can be flexibly integrated with the existing loss functions and network architectures. Extensive experiments on various benchmarks of face recognition show the proposed method significantly improves the training, not only in shallo
赦免
发表于 2025-3-31 07:30:36
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极微小
发表于 2025-3-31 12:52:00
Sebastian Wieczorek,Vitaly Kozyura,Wei Wei,Andreas Roth,Alin Stefanescu as all-sample universal attack. In addition, we propose a data-free TND, which can detect a TrojanNet without accessing any data samples. We show that such a TND can be built by leveraging the internal response of hidden neurons, which exhibits the Trojan behavior even at random noise inputs. The e
bronchodilator
发表于 2025-3-31 13:21:01
Aryldo G. Russo Jr.ng aspects such as head pose, facial expression, hair style, illumination, and many others which are very hard to annotate in real data. The real images, which are presented to the network without labels, extend the variety of the generated images and encourage realism. Finally, we propose an evalua
时代
发表于 2025-3-31 19:46:51
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