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

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樓主: chondrocyte
41#
發(fā)表于 2025-3-28 18:23:36 | 只看該作者
Fertilit?tsst?rungen beim Manneification lane semantic segmentation suffer from low segmentation accuracy for special lanes (e.g., ramp, emergency lane) and lane lines. To address this problem, we propose a cross-layer multi-class lane semantic segmentation model called CLASPPNet (Cross-Layer Atrous Spatial Pyramid Pooling Networ
42#
發(fā)表于 2025-3-28 22:25:08 | 只看該作者
Fertilization Mechanisms in Man and Mammalsn deep learning with excellent performance, but their memory and computation costs hinder practical applications. In this paper, we propose a down-up sampling continuous mutual affine super-resolution network (DUSCMAnet) to solve above problems. Moreover, we propose a classification-based SR algorit
43#
發(fā)表于 2025-3-29 01:03:17 | 只看該作者
Fusion of the Sperm with the Vitellus,methods for detecting and locating such tampering. Previous studies have mainly focused on the supervisory role of the mask on the model. The mask edges contain rich complementary signals, which help to fully understand the image and are usually ignored. In this paper, we propose a new network named
44#
發(fā)表于 2025-3-29 04:15:27 | 只看該作者
45#
發(fā)表于 2025-3-29 08:23:56 | 只看該作者
46#
發(fā)表于 2025-3-29 12:38:15 | 只看該作者
N. Bagni,A. Tassoni,M. Franceschettid domain adaptation is proved to be effective on this problem in recent researches. Unsupervised domain adaptive object detection of students’ heads between different classrooms has becoming an important task with the development of Smart Classroom. However, few cross-classroom models for students’
47#
發(fā)表于 2025-3-29 16:24:30 | 只看該作者
N. Bagni,A. Tassoni,M. Franceschettiing text-driven image manipulation is typically implemented by GAN inversion or fine-tuning diffusion models. The former is limited by the inversion capability of GANs, which fail to reconstruct pictures with novel poses and perspectives. The latter methods require expensive optimization for each in
48#
發(fā)表于 2025-3-29 20:16:10 | 只看該作者
49#
發(fā)表于 2025-3-30 01:16:54 | 只看該作者
https://doi.org/10.1007/978-3-031-44210-0artificial neural networks (NN); machine learning; deep learning; federated learning; convolutional neur
50#
發(fā)表于 2025-3-30 04:45:30 | 只看該作者
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