Arama Sonuçları - "generative adversarial networks"

Deep Learning-Based Face Analytics. Yazar: Ratha, Nalini K.

İçindekiler: “…10.5.1 Training Details -- 10.5.2 Methods for Evaluation -- 10.5.3 Results on IJB-A Dataset -- 10.5.4 Results on YouTube Face dataset -- 10.5.5 Results on Celebrity-1000 Dataset -- 10.6 Conclusions -- References -- 11 Thermal-to-Visible Face Synthesis and Recognition -- 11.1 The Infrared Spectrum -- 11.2 GAN-Based Synthesis of Visible Faces From Thermal Faces -- 11.2.1 Generative Adversarial Networks (GANs) -- 11.2.2 GAN-Based Synthesis Network -- 11.3 Synthesis of High-Quality Visible Faces From Polarimetric Thermal Faces Using GANs -- 11.4 Thermal-to-Visible Face Verification Via Attribute-Preserved Synthesis -- 11.5 Self-attention Guided Synthesis -- 11.6 Conclusion -- References -- 12 Obstructing DeepFakes by Disrupting Face Detection and Facial Landmarks Extraction -- 12.1 Introduction -- 12.2 Background and Related Works -- 12.3 Attacking Face Detectors -- 12.3.1 White-Box Adversarial Perturbation Generation -- 12.3.2 Gray-Box Adversarial Perturbation Generation -- 12.3.3 Black-Box Adversarial Perturbation Generation -- 12.4 Attacking Facial Landmark Extractors -- 12.5 Experiments -- 12.5.1 Attacking Face Detection -- 12.5.2 Attacking Landmark Extractors -- 12.6 Conclusion -- References -- 13 Multi-channel Face Presentation Attack Detection Using Deep Learning -- 13.1 Introduction -- 13.2 Related Work -- 13.2.1 Feature-Based Approaches for Face PAD -- 13.2.2 CNN-Based Approaches for Face PAD -- 13.2.3 One-Class Models for Face PAD -- 13.2.4 Multi-channel-Based Approaches for Face PAD -- 13.2.5 Challenges in PAD -- 13.3 Proposed Method -- 13.3.1 Preprocessing -- 13.3.2 Network Architecture -- 13.3.3 One-Class Contrastive Loss (OCCL) -- 13.3.4 Implementation Details -- 13.4 Experiments -- 13.4.1 WMCA Dataset -- 13.4.2 MLFP Dataset -- 13.4.3 SiW-M Dataset -- 13.4.4 Evaluation Metrics -- 13.4.5 Baselines.…”
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