Advances in Visual Computing
15th International Symposium, ISVC 2020, San Diego, CA, USA, October 5-7, 2020, Proceedings, Part I
By George Bebis, Richard Boyle, Darko Koracin and Bahram Parvin
"Advances in Visual Computing" was published by Springer International Publishing AG in 2020 - Cham, it has 1 pages and the language of the book is English.
“Advances in Visual Computing” Metadata:
- Title: Advances in Visual Computing
- Authors: George BebisRichard BoyleDarko KoracinBahram Parvin
- Language: English
- Number of Pages: 1
- Publisher: ➤ Springer International Publishing AG
- Publish Date: 2020
- Publish Location: Cham
“Advances in Visual Computing” Subjects and Themes:
- Subjects: ➤ Visualization - Visual programming (Computer science) - Congresses - Virtual reality - Data processing - Computer science - Computer graphics - Computer vision - Optical pattern recognition - Bioinformatics - Pattern Recognition - Image Processing and Computer Vision - User Interfaces and Human Computer Interaction - Information Systems Applications (incl. Internet) - Computational Biology/Bioinformatics
Edition Specifications:
- Weight: 1.181
- Pagination: xxxvi, 745
Edition Identifiers:
- The Open Library ID: OL37318731M - OL16930512W
- ISBN-13: 9783030645557 - 9783030645564
- All ISBNs: 9783030645557 - 9783030645564
AI-generated Review of “Advances in Visual Computing”:
"Advances in Visual Computing" Description:
Open Data:
Intro -- Preface -- Organization -- Abstracts of Keynote Talks -- Can Computers Create Art? -- Spatial Perception and Presence in Virtual Architectural Environments -- The Shape of Art History in the Eyes of the Machine -- Object-Oriented Image Stitching -- Fun with Visualization in the Data Deluge -- Understanding Visual Appearance from Micron to Global Scale -- Contents - Part I -- Contents - Part II -- Deep Learning -- Regularization and Sparsity for Adversarial Robustness and Stable Attribution -- 1 Introduction -- 2 Background -- 2.1 Related Work in Adversarial Attacks -- 2.2 Related Work in Image Attribution -- 3 Methodology -- 3.1 Image Classification Architectures -- 3.2 Adversarial Attacks -- 3.3 Attribution Methods -- 4 Experiments and Results -- 4.1 Sparse Regularization Effect on Average Density -- 4.2 Robustness Against Adversarial Attacks -- 4.3 Stability in Class Attribution -- 5 Conclusion -- References -- Self-Competitive Neural Networks -- 1 Introduction -- 2 Related Work -- 3 Self-Competitive Neural Network -- 3.1 SCNN Architecture -- 4 Details of SCNN -- 5 Experimental Results -- 5.1 Limited MNIST -- 5.2 Fashion MNIST -- 5.3 Cifar10 -- 6 Conclusion and Future Work -- References -- A Novel Contractive GAN Model for a Unified Approach Towards Blind Quality Assessment of Images from Heterogeneous Sources -- 1 Introduction -- 2 Contractive Generative Adversarial Learning for Unified Quality Assessment -- 2.1 Formulation -- 2.2 A Unified Blind Image Quality Assessment Model Using C-GAN -- 3 Experiments -- 4 Conclusion -- References -- Nonconvex Regularization for Network Slimming: Compressing CNNs Even More -- 1 Introduction -- 2 Related Works -- 2.1 Compression Techniques for CNNs -- 2.2 Regularization Penalty -- 3 Proposed Method -- 3.1 Batch Normalization Layer -- 3.2 Network Slimming with Nonconvex Sparse Regularization
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