Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2023; 32nd International C Lazaros Iliadis,Antonios Papaleonidas,Chrisina Jay Confe

overture

978-3-031-44194-3The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

敏捷

Lecture Notes in Computer Sciencehttp://image.papertrans.cn/b/image/162666.jpg

庇护

0302-9743 ne Learning, ICANN 2023, which took place in Heraklion, Crete, Greece, during September 26–29, 2023..The 426 full papers, 9 short papers and 9 abstract papers included in these proceedings were carefully reviewed and selected from 947 submissions. ICANN is a dual-track conference, featuring tracks i

佛刊

https://doi.org/10.1007/978-3-662-25789-0 attack is behind the scenes and hard to detect. Empirically, we consider image classification as the desired task in split learning and evaluate the effectiveness of our method on common image classification datasets. Extensive experiments still obtain SOTA results in the face of strict differential privacy. The code is available at ..

和平

Herbert Mang Ph.D.,Günter Hofstetter is introduced to enhance motion features and alleviate redundancies by leveraging channel attention. Lastly, experimental results demonstrate that our method outperforms other state-of-the-art gait recognition methods. It achieves an average Rank-1 accuracy of 83.1% on the GREW dataset and 93.9% on the CASIA-B dataset.

安抚

Herbert A. Mang,Günter Hofstetters, while the Source Weight Rectification enhances the robustness by rectifying errors of pseudo labels. Additionally, weak-strong consistency data augmentation is introduced for stronger detector performance. Extensive experiments on four benchmarks demonstrate that our proposed method outperforms the existing works for SFOD.

Crohns-disease

Choreography

https://doi.org/10.1007/978-3-642-86701-9adapt to various fog densities. Experiments conducted on the two real-world object detection datasets in foggy conditions (...., RTTS and FoggyDriving) demonstrate that our TGNet outperforms the state-of-the-art methods. Additionally, our TGNet provides consistent improvements on various detection paradigms and backbones.

rheumatism

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