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Advances in Communication of Design Proceedings of the AHFE 2017 International Conference on Human Factors in Communication of Design, July 17−21, 2017, The Westin Bonaventure Hote...

İçindekiler: “…Humanistic teaching approaches in typographic communication design -- Directing untrained users' attention using simple sound patterns -- Looking forward to reverse image search: Measuring the effectiveness of reverse image searches in online help -- Teaching methodologies in communication design -- Ergonomics and organizational communication: Methods for business communication auditing -- Trust building privacy preferences for young adults visiting political campaign websites -- The style of imagery: A color study of landscape paintings -- The effectiveness of message sidedness on trust in check-in advertising -- Towards a generalization of new media -- Calligraphy practice app, system and tool for learning calligraphy -- Smart device-based notifications: A survey on user's satisfaction of traditional notification mechanisms -- The challenge of online meal ordering platforms: Website design and credibility, customers' trust, and food safety -- Consistency of visual information in web design: Focusing on responsiveness of a university website -- Awareness collaboration with uses and designers: Metaphor analysis and expression of spatial image schemes with sign language. .…”
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Deep Learning-Based Face Analytics. Yazar: Ratha, Nalini K.

İçindekiler: “…13.4.6 Experiments and Results in WMCA Dataset -- 13.4.7 Experiments and Results in MLFP Dataset -- 13.4.8 Experiments and Results in SiW-M Dataset -- 13.4.9 Cross-Database Evaluations -- 13.5 Discussions -- 13.6 Conclusions -- References -- 14 Scalable Person Re-identification: Beyond Supervised Approaches -- 14.1 Introduction -- 14.2 Related Work -- 14.3 Optimal Subset Selection for Labeling -- 14.3.1 Problem Statement -- 14.3.2 Solution Overview -- 14.3.3 Sample Experimental Results -- 14.4 On-Boarding New Cameras through Transfer Learning -- 14.4.1 Problem Statement -- 14.4.2 Solution Overview -- 14.4.3 Sample Experimental Results -- 14.5 Conclusions -- References -- 15 Towards Causal Benchmarking of Bias in Face Analysis Algorithms -- 15.1 Introduction -- 15.2 Related Work -- 15.3 Face Attribute Annotation in Synthetic Images -- 15.4 Method -- 15.4.1 Transects: A Walk in Face Space -- 15.4.2 Analyses Using Transects -- 15.4.3 Human Annotation -- 15.5 Experiments -- 15.5.1 Gender Classifiers -- 15.5.2 Transect Data -- 15.5.3 Comparison of Transects to Real Face Datasets -- 15.5.4 Analysis of Bias -- 15.5.5 Regression Analysis -- 15.6 Discussion and Conclusions -- 15.6.1 Summary -- 15.6.2 Limitations and Future Work -- References -- 16 Strategies of Face Recognition by Humans and Machines -- 16.1 Introduction -- 16.2 Identification Accuracy: Human Face Recognition -- 16.2.1 Face Identification Performance of Untrained Humans -- 16.2.2 Performance of Trained Versus Untrained Humans -- 16.3 Identification Accuracy: Machines Versus Humans -- 16.4 Fusion -- 16.4.1 Crowd-Sourcing Methods -- 16.4.2 Fusion: Human Participants -- 16.4.3 Fusion: Humans and Machines -- 16.5 Strategic Differences: Forensic Facial Examiners Versus Untrained Humans -- 16.6 Other-Race Effects in Humans and Machines -- 16.6.1 Theories of the Other-Race Effect.…”
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Deep Learning with TensorFlow and Keras - 3rd Edition : Build and Deploy Supervised, Unsupervised, Deep, and Reinforcement Learning Models. Yazar: Kapoor, Amita

İçindekiler: “…Context autoencoder -- Colorization -- Innate relationship prediction -- Relative position -- Solving jigsaw puzzles -- Rotation -- Hybrid self-prediction -- VQ-VAE -- Jukebox -- DALL-E -- VQ-GAN -- Contrastive learning -- Training objectives -- Contrastive loss -- Triplet loss -- N-pair loss -- Lifted structural loss -- NCE loss -- InfoNCE loss -- Soft nearest neighbors loss -- Instance transformation -- SimCLR -- Barlow Twins -- BYOL -- Feature clustering -- DeepCluster -- SwAV -- InterCLR -- Multiview coding -- AMDIM -- CMC -- Multimodal models -- CLIP -- CodeSearchNet -- Data2Vec -- Pretext tasks -- Summary -- References -- Chapter 11: Reinforcement Learning -- An introduction to RL -- RL lingo -- Deep reinforcement learning algorithms -- How does the agent choose its actions, especially when untrained? -- How does the agent maintain a balance between exploration and exploitation? …”
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