標(biāo)題: Titlebook: Binary Representation Learning on Visual Images; Learning to Hash for Zheng Zhang Book 2024 The Editor(s) (if applicable) and The Author(s) [打印本頁] 作者: 烈酒 時(shí)間: 2025-3-21 19:04
書目名稱Binary Representation Learning on Visual Images影響因子(影響力)
書目名稱Binary Representation Learning on Visual Images影響因子(影響力)學(xué)科排名
書目名稱Binary Representation Learning on Visual Images網(wǎng)絡(luò)公開度
書目名稱Binary Representation Learning on Visual Images網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Binary Representation Learning on Visual Images被引頻次
書目名稱Binary Representation Learning on Visual Images被引頻次學(xué)科排名
書目名稱Binary Representation Learning on Visual Images年度引用
書目名稱Binary Representation Learning on Visual Images年度引用學(xué)科排名
書目名稱Binary Representation Learning on Visual Images讀者反饋
書目名稱Binary Representation Learning on Visual Images讀者反饋學(xué)科排名
作者: ELUDE 時(shí)間: 2025-3-21 23:22 作者: Arbitrary 時(shí)間: 2025-3-22 02:36 作者: Root494 時(shí)間: 2025-3-22 06:09 作者: MEET 時(shí)間: 2025-3-22 11:27 作者: Gudgeon 時(shí)間: 2025-3-22 13:51
Metallniederschl?ge und Metallf?rbungenhor-induced asymmetric graph learning model. Additionally, a sparsity-guided selective quantization function minimizes space transformation losses, while a regressive semantic function enhances the flexibility of formulated semantics in hash code learning. The joint learning objective concurrently p作者: AXIS 時(shí)間: 2025-3-22 17:34
Metallo-Supramolecular Polymerssimilarities during the feature learning process. Additionally, well-designed latent subspace learning is incorporated to acquire noise-free latent features based on sparse-constrained supervised learning, fully leveraging the latent under-explored characteristics of data in subspace construction. L作者: 彩色 時(shí)間: 2025-3-22 21:26 作者: 芳香一點(diǎn) 時(shí)間: 2025-3-23 04:25
Topics in Organometallic Chemistrycriminative and semantic properties jointly. Adversarial examples are generated by maximizing the Hamming distance between hash codes of adversarial samples and mainstay features, validated for efficacy in adversarial attack trials. Notably, this chapter formulates the formalized adversarial trainin作者: JOG 時(shí)間: 2025-3-23 09:00 作者: Multiple 時(shí)間: 2025-3-23 10:43
Scalable Supervised Asymmetric Hashing,earns two distinctive hashing functions by minimizing regression loss for semantic label alignment and encoding loss for refined latent features. Notably, instead of utilizing only partial similarity correlations, SSAH directly employs the full-pairwise similarity matrix to prevent information loss 作者: inculpate 時(shí)間: 2025-3-23 14:29 作者: defuse 時(shí)間: 2025-3-23 22:02 作者: 嘮叨 時(shí)間: 2025-3-24 00:49
Ordinal-Preserving Latent Graph Hashing,similarities during the feature learning process. Additionally, well-designed latent subspace learning is incorporated to acquire noise-free latent features based on sparse-constrained supervised learning, fully leveraging the latent under-explored characteristics of data in subspace construction. L作者: appall 時(shí)間: 2025-3-24 03:50 作者: Misgiving 時(shí)間: 2025-3-24 08:53
Semantic-Aware Adversarial Training,criminative and semantic properties jointly. Adversarial examples are generated by maximizing the Hamming distance between hash codes of adversarial samples and mainstay features, validated for efficacy in adversarial attack trials. Notably, this chapter formulates the formalized adversarial trainin作者: 否認(rèn) 時(shí)間: 2025-3-24 13:19
shing techniques. These approaches can empower readers to proficiently grasp the fundamental principles of the traditional and state-of-the-art methods in binary representations, modeling, and learning. The the978-981-97-2114-6978-981-97-2112-2作者: Hay-Fever 時(shí)間: 2025-3-24 16:41 作者: 按等級(jí) 時(shí)間: 2025-3-24 22:20 作者: negotiable 時(shí)間: 2025-3-25 02:47 作者: 有偏見 時(shí)間: 2025-3-25 04:19
Probability Ordinal-Preserving Semantic Hashing,ten neglecting the mutual triplet-level ordinal structure crucial for similarity preservation. This chapter introduces a groundbreaking approach–the Probability Ordinal-preserving Semantic Hashing (POSH) framework–pioneering ordinal-preserving hashing under a non-parametric Bayesian theory. The fram作者: 在駕駛 時(shí)間: 2025-3-25 10:09
Ordinal-Preserving Latent Graph Hashing, samples in the visual space to generate discriminative hash codes. However, these approaches neglect the intrinsic latent features within the high-dimensional feature space, making it challenging to capture the underlying topological structure of data and resulting in suboptimal hash codes for imag作者: Host142 時(shí)間: 2025-3-25 15:37
Deep Collaborative Graph Hashing,and the computational efficiency of compact hash code learning. However, existing deep semantic-preserving hashing approaches often treat semantic labels as ground truth for classification or transform them into prevalent pairwise similarities, overlooking interactive correlations between visual sem作者: 免除責(zé)任 時(shí)間: 2025-3-25 17:55 作者: Isolate 時(shí)間: 2025-3-25 20:14 作者: Catheter 時(shí)間: 2025-3-26 01:40 作者: 梯田 時(shí)間: 2025-3-26 05:52 作者: 橢圓 時(shí)間: 2025-3-26 10:11
978-981-97-2114-6The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor作者: Insubordinate 時(shí)間: 2025-3-26 14:08
Zheng ZhangBroadens the understanding of binary representation learning in the context of visual data.Offers the latest research trends in binary representation, modeling and learning.Expounds the potential, int作者: molest 時(shí)間: 2025-3-26 20:25
http://image.papertrans.cn/b/image/192679.jpg作者: Awning 時(shí)間: 2025-3-26 22:44 作者: 密切關(guān)系 時(shí)間: 2025-3-27 01:43
Metallniederschl?ge und Metallf?rbungenowever, mastering discriminative binary codes that perfectly preserve full-pairwise similarities in high-dimensional real-valued features remains a challenging task for ensuring optimal performance. To tackle this challenge, this chapter introduces a novel method, Scalable Supervised Asymmetric Hash作者: 窩轉(zhuǎn)脊椎動(dòng)物 時(shí)間: 2025-3-27 09:18 作者: Detonate 時(shí)間: 2025-3-27 11:34 作者: 無瑕疵 時(shí)間: 2025-3-27 16:11
Metallo-Supramolecular Polymers samples in the visual space to generate discriminative hash codes. However, these approaches neglect the intrinsic latent features within the high-dimensional feature space, making it challenging to capture the underlying topological structure of data and resulting in suboptimal hash codes for imag作者: 異端邪說下 時(shí)間: 2025-3-27 19:11
Hydrozirconation and Its Applications,and the computational efficiency of compact hash code learning. However, existing deep semantic-preserving hashing approaches often treat semantic labels as ground truth for classification or transform them into prevalent pairwise similarities, overlooking interactive correlations between visual sem作者: 身體萌芽 時(shí)間: 2025-3-27 23:48 作者: 憤世嫉俗者 時(shí)間: 2025-3-28 04:19
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