標(biāo)題: Titlebook: Deep Learning Theory and Applications; First International Ana Fred,Carlo Sansone,Kurosh Madani Conference proceedings 2023 The Editor(s) [打印本頁] 作者: Falter 時(shí)間: 2025-3-21 17:00
書目名稱Deep Learning Theory and Applications影響因子(影響力)
書目名稱Deep Learning Theory and Applications影響因子(影響力)學(xué)科排名
書目名稱Deep Learning Theory and Applications網(wǎng)絡(luò)公開度
書目名稱Deep Learning Theory and Applications網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Deep Learning Theory and Applications被引頻次
書目名稱Deep Learning Theory and Applications被引頻次學(xué)科排名
書目名稱Deep Learning Theory and Applications年度引用
書目名稱Deep Learning Theory and Applications年度引用學(xué)科排名
書目名稱Deep Learning Theory and Applications讀者反饋
書目名稱Deep Learning Theory and Applications讀者反饋學(xué)科排名
作者: 眨眼 時(shí)間: 2025-3-21 23:42 作者: 脫落 時(shí)間: 2025-3-22 04:06
,Evaluating Deep Learning Models for?the?Automatic Inspection of?Collective Protective Equipment,heir performances in specific scenarios..In this paper we tackle the problem of autonomously inspecting the conditions of Collective Protection Equipment (CPE) such as fire extinguishers, warning signs, ground and wall signalization and others..Work ministry imposes that such CPE are in good conditi作者: 字的誤用 時(shí)間: 2025-3-22 08:07 作者: 四溢 時(shí)間: 2025-3-22 10:37
,Forecasting the?UN Sustainable Development Goals,le Development Goal (SDG) attainment forecasting. Unlike earlier SDG attainment forecasting frameworks, the SDG-TTF framework considers the possibility for causal relationships between SDG indicators, both within a given geographic entity (intra-entity relationships) and between the current entity a作者: 淘氣 時(shí)間: 2025-3-22 14:58 作者: 淘氣 時(shí)間: 2025-3-22 17:57 作者: engrave 時(shí)間: 2025-3-22 23:49
,Alternative Data Augmentation for?Industrial Monitoring Using Adversarial Learning, labels are translated into color images using pix2pix and used to train a U-Net. The results suggest that the trigonometric function is superior to the WGAN model. However, a precise examination of the resulting images indicate that WGAN and image-to-image translation achieve good segmentation resu作者: 瑣事 時(shí)間: 2025-3-23 04:59
,Multi-stage Conditional GAN Architectures for?Person-Image Generation, Multi-stage Person Generation (MPG) model, in which we have modified the Generator architecture of Pose Guided Person Image Generation . resulting in two approaches. The first three-stage person generation approach has an additional generator integrated to base architecture and has trained the mode作者: Stress-Fracture 時(shí)間: 2025-3-23 07:00
,Evaluating Deep Learning Models for?the?Automatic Inspection of?Collective Protective Equipment,e evaluation of CPE conditions. We provide results that highlight each architecture’s advantages and drawbacks in the aforementioned scenario..Indeed, experiments have shown their potential in reducing time and costs of periodic inspections in factories.作者: Lamina 時(shí)間: 2025-3-23 09:44 作者: vocation 時(shí)間: 2025-3-23 14:25
Crack Detection on Brick Walls by Convolutional Neural Networks Using the Methods of Sub-dataset Gemage with the sub-datasets. In this study, sub-dataset generation and matching methods are proposed to improve the performance of crack detection in brick walls using CNN. CNN training is conducted with each sub-dataset generated by the proposed sub-dataset generation method, while crack detection i作者: ingenue 時(shí)間: 2025-3-23 21:15
Lecture Notes in Computer Science labels are translated into color images using pix2pix and used to train a U-Net. The results suggest that the trigonometric function is superior to the WGAN model. However, a precise examination of the resulting images indicate that WGAN and image-to-image translation achieve good segmentation resu作者: 大包裹 時(shí)間: 2025-3-23 23:02
Ya Wang,Qiuyu Zhang,Lizhi Zhang,Yunyan Hu Multi-stage Person Generation (MPG) model, in which we have modified the Generator architecture of Pose Guided Person Image Generation . resulting in two approaches. The first three-stage person generation approach has an additional generator integrated to base architecture and has trained the mode作者: 受辱 時(shí)間: 2025-3-24 02:35 作者: FLINT 時(shí)間: 2025-3-24 10:32 作者: semble 時(shí)間: 2025-3-24 11:55 作者: Adulterate 時(shí)間: 2025-3-24 14:54 作者: 熄滅 時(shí)間: 2025-3-24 20:22
Conference proceedings 2023ence in real-world applications such as computer vision, information retrieval and summarization from structuredand unstructured multimodal data sources, natural language understanding andtranslation, and many other application domains..作者: Prognosis 時(shí)間: 2025-3-25 01:56
1865-0929 l intelligence in real-world applications such as computer vision, information retrieval and summarization from structuredand unstructured multimodal data sources, natural language understanding andtranslation, and many other application domains..978-3-031-37319-0978-3-031-37320-6Series ISSN 1865-0929 Series E-ISSN 1865-0937 作者: epicondylitis 時(shí)間: 2025-3-25 06:40
1865-0929 ng Theory and Applications,?DeLTA 2020 and?DeLTA 2021,?was held virtually due to the COVID-19 crisis on?July 8-10, 2020 and?July 7–9, 2021..The 7 full papers included in this book were carefully reviewed and?selected from 58 submissions. They present recent research on machine learning and artificia作者: ANN 時(shí)間: 2025-3-25 10:30
Voraphan Vorakitphan,Takashi Ohtanly discrete annotation labels. This greatly hinders the learning and modeling of smooth transitions between displayed affective states. To overcome this challenge, we explore the potential of label interpolation to enhance networks trained on categorical datasets with the ability to generate images conditioned on continuous features.作者: 劇本 時(shí)間: 2025-3-25 11:48 作者: Immunoglobulin 時(shí)間: 2025-3-25 16:29
,Intercategorical Label Interpolation for?Emotional Face Generation with?Conditional Generative Advenly discrete annotation labels. This greatly hinders the learning and modeling of smooth transitions between displayed affective states. To overcome this challenge, we explore the potential of label interpolation to enhance networks trained on categorical datasets with the ability to generate images conditioned on continuous features.作者: VOC 時(shí)間: 2025-3-25 22:43 作者: 輕信 時(shí)間: 2025-3-26 03:17
Communications in Computer and Information Sciencehttp://image.papertrans.cn/d/image/264587.jpg作者: 背叛者 時(shí)間: 2025-3-26 08:14
Deep Learning Theory and Applications978-3-031-37320-6Series ISSN 1865-0929 Series E-ISSN 1865-0937 作者: Bridle 時(shí)間: 2025-3-26 11:57
Lecture Notes in Computer Scienceels have gained importance since they allow for more precise examination. These models, however, require large image datasets in order to achieve a fair accuracy level. In some cases, training data is sparse or lacks of sufficient annotation, a fact that especially applies to highly specialized prod作者: 會(huì)議 時(shí)間: 2025-3-26 14:43
Ya Wang,Qiuyu Zhang,Lizhi Zhang,Yunyan Hu, and Games. Advancements in Artificial Intelligence (AI) and Machine learning (ML) lead to the rapid growth of integrating every aspect into AI and ML. There are many deep learning models like Variational Auto Encoders (VAE), Stacked Hourglass networks and Generative Adversarial Networks (GANs) for作者: 玩笑 時(shí)間: 2025-3-26 20:26
Dai Luo,Xiangcheng Wei,Le Changheir performances in specific scenarios..In this paper we tackle the problem of autonomously inspecting the conditions of Collective Protection Equipment (CPE) such as fire extinguishers, warning signs, ground and wall signalization and others..Work ministry imposes that such CPE are in good conditi作者: Explicate 時(shí)間: 2025-3-26 21:37 作者: 不連貫 時(shí)間: 2025-3-27 03:59 作者: reception 時(shí)間: 2025-3-27 06:15 作者: Exclaim 時(shí)間: 2025-3-27 09:54
Lecture Notes in Computer Sciencemportant role in ensuring the safety and durability of structures. Conventionally, a maintenance engineer performs crack detection manually, which is laborious and time-consuming. Therefore, a systematic crack detection method is required. Among the existing crack detection methods, convolutional ne作者: 鋼盔 時(shí)間: 2025-3-27 15:49
https://doi.org/10.1007/978-3-031-37320-6Models and Algorithms; Machine Learning; Big Data Analytics; Computer Vision; Natural Language Understan作者: obstinate 時(shí)間: 2025-3-27 18:00 作者: Ventilator 時(shí)間: 2025-3-28 01:41 作者: Offbeat 時(shí)間: 2025-3-28 03:19 作者: 解脫 時(shí)間: 2025-3-28 07:45 作者: Override 時(shí)間: 2025-3-28 13:37
https://doi.org/10.1007/978-3-663-01804-9Arbeit; Bundesrat; Bundestag; DDR; Europa; Geschichte; Gesellschaft; Grundgesetz; Kultur; Mitteleuropa; Politi
