D S. M. Kamruzzaman, Firoz Ahmed Siddiqi, Md. Introduction to Presentation Attack Detection in Face Biometrics and Recent Advances, Algorithmic Fairness in Face Morphing Attack Detection, Consistency Regularization for Deep Face Anti-Spoofing, Face Presentation Attack Detection using Taskonomy Feature, OTB-morph: One-Time Biometrics via Morphing applied to Face Templates, Dual Spoof Disentanglement Generation for Face Anti-spoofing with Depth Uncertainty Learning, Graph-based Generative Face Anonymisation with Pose Preservation, Does a Face Mask Protect my Privacy? Fanzi Wu, Linchao Bao, Yajing Chen, Yonggen Ling, Yibing Song, Songnan Li, King Ngi Ngan, Wei Liu . Ahmed ElSayed, Elif Kongar, Ausif Mahmood, Tarek Sobh, Terrance Boult . Generative adversarial nets can be extended to a conditional model if both the generator and discriminator are conditioned on some extra information y. y could be any kind of auxiliary information, such as class labels or data from other modalities. Huilin Yang, Junyan Lyu, Pujin Cheng, Xiaoying Tang . Xiaoguang Tu, Jian Zhao, Zihang Jiang, Yao Luo, Mei Xie, Yang Zhao, Linxiao He, Zheng Ma, Jiashi Feng . Mostafa Mehdipour Ghazi, Hazim Kemal Ekenel . Robust Feature Matching for Remote Sensing Image Registration via Linear Adaptive Filtering,IEEE Transactions on Geoscience and Remote Sensing, 59(2), pp. Minh Ng, Burak Mandira, Selim Frat Ylmaz, Ward Heij, Sezer Karaoglu, Henri Bouma, Hamdi Dibeklioglu, Theo Gevers . Hao Zhu, Haotian Yang, Longwei Guo, Yidi Zhang, Yanru Wang, Mingkai Huang, Qiu Shen, Ruigang Yang, Xun Cao . 172. Weidong Shi, Guanghui Ren, Yunpeng Chen, Shuicheng Yan . Autoencoders, minimum description length and Helmholtz free energy. Mohamed Mohana, Prasanalakshmi B, Salem Alelyani, Mohammed Saleh Alsaqer . 1674-1684, 2016.IF=5.859ESI Hot Papers & ESI Highly Cited Papers Jiayi Ma, Yong Ma, and Chang Li. Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery,IEEE Transactions on Computational Imaging, 6, pp. Yuhao Zhu, Qi Li, Jian Wang, Chengzhong Xu, Zhenan Sun . Work fast with our official CLI. Alternately, when the generator fools the discriminator, it is rewarded, or no change is needed to the model parameters, but the discriminator is penalized and its model parameters are updated. Fig. Paarth Neekhara, Shehzeen Hussain, Xinqiao Zhang, Ke Huang, Julian McAuley, Farinaz Koushanfar . SRLSP: A Face Image Super-Resolution Algorithm Using Smooth Regression with Local Structure Prior,IEEE Transactions on Multimedia, 19(1), pp. Unified gradient- and intensity-discriminator generative adversarial network for image fusion,Information Fusion, 88, pp. Shifeng Zhang, Ajian Liu, Jun Wan, Yanyan Liang, Guogong Guo, Sergio Escalera, Hugo Jair Escalante, Stan Z. Li . Philipp Terhrst, Jan Niklas Kolf, Naser Damer, Florian Kirchbuchner, Arjan Kuijper . Jerone T. A. Andrews, Thomas Tanay, Lewis D. Griffin . Welcome to this 1.5 hours long hands-on project on Image Super Resolution using Autoencoders in Keras. f arXiv:2106.15282, arXiv, 17 Dec. 2021, [11] Weng, Lilian. [73] Alex Martelli, a Fellow at the Python Software Foundation and Python book author, wrote: "To describe something as 'clever' is not considered a compliment in the Python culture."[74]. DatasetAndreas Rssler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, Matthias Niener . Michael Grupp, Philipp Kopp, Patrik Huber, Matthias Rtsch . , let the actual sparsity of activation in each layer The diffusion process can be formulated as an SDE. The dataset contains about 1.7 million faces, 59k identities, which is manually cleaned from 2.0 million raw images. 0 , and refer to it as the code, the latent variable, latent representation, latent vector, etc. Michael J. Wilber, Vitaly Shmatikov, Serge Belongie . Generative Adversarial Networks, or GANs for short, are an approach to generative modeling using deep learning methods, such as convolutional neural networks. [ Qingyu Song, Changan Wang, Yabiao Wang, Ying Tai, Chengjie Wang, Jilin Li, Jian Wu, andJiayi Ma. Haibo Qiu, Baosheng Yu, Dihong Gong, Zhifeng Li, Wei Liu, Dacheng Tao . For many research papers, there will be one foundational, must-read kind of paper. A-Softmax lossAngular marginWeiyang Liu, Yandong Wen, Zhiding Yu, Ming Li, Bhiksha Raj, Le Song . employed a U-Net. -- Vulnerability and Detection, Delving into the Adversarial Robustness on Face Recognition, The UU-Net: Reversible Face De-Identification for Visual Surveillance Video Footage, ECML: An Ensemble Cascade Metric Learning Mechanism towards Face Verification, Loss Function Search for Face Recognition, Improving Face Recognition by Clustering Unlabeled Faces in the Wild, SqueezeFacePoseNet: Lightweight Face Verification Across Different Poses for Mobile Platforms, FaceHop: A Light-Weight Low-Resolution Face Gender Classification Method, NPCFace: A Negative-Positive Cooperation Supervision for Training Large-scale Face Recognition, Multi-Metric Evaluation of Thermal-to-Visual Face Recognition, The Effect of Wearing a Mask on Face Recognition Performance: an Exploratory Study, HyperFaceNet: A Hyperspectral Face Recognition Method Based on Deep Fusion, Subclass Contrastive Loss for Injured Face Recognition, Hybrid Score- and Rank-level Fusion for Person Identification using Face and ECG Data, Domain Private and Agnostic Feature for Modality Adaptive Face Recognition, BioMetricNet: deep unconstrained face verification through learning of metrics regularized onto Gaussian distributions, BroadFace: Looking at Tens of Thousands of People at Once for Face Recognition, MaskedFace-Net -- A Dataset of Correctly/Incorrectly Masked Face Images in the Context of COVID-19, Cross-Domain Identification for Thermal-to-Visible Face Recognition, Masked Face Recognition for Secure Authentication, Inducing Predictive Uncertainty Estimation for Face Recognition, Red Carpet to Fight Club: Partially-supervised Domain Transfer for Face Recognition in Violent Videos, The FaceChannel: A Fast & Furious Deep Neural Network for Facial Expression Recognition, FairFace Challenge at ECCV 2020: Analyzing Bias in Face Recognition, DVG-Face: Dual Variational Generation for Heterogeneous Face Recognition, BWCFace: Open-set Face Recognition using Body-worn Camera, Video Face Recognition System: RetinaFace-mnet-faster and Secondary Search, The Elements of End-to-end Deep Face Recognition: A Survey of Recent Advances, Learning Emotional-Blinded Face Representations, DBLFace: Domain-Based Labels for NIR-VIS Heterogeneous Face Recognition, Are Adaptive Face Recognition Systems still Necessary? AU - Chan, Yui Lam. in the code space is used to encode a message Cho, K. (2013, February). In, List of datasets for machine-learning research, "Nonlinear principal component analysis using autoassociative neural networks", "Modeling word perception using the Elman network", "Nonlinear Autoassociation Is Not Equivalent to PCA", "Training Methods for Adaptive Boosting of Neural Networks". Wenbin Zhu, Chien-Yi Wang, Kuan-Lun Tseng, Shang-Hong Lai, Baoyuan Wang . Arnaud Dapogny, Kvin Bailly, Matthieu Cord . Adnane Cabani, Karim Hammoudi, Halim Benhabiles, Mahmoud Melkemi . Although there is a rough schedule for each release, they are often delayed if the code is not ready. Mingtao Feng, Syed Zulqarnain Gilani, Yaonan Wang, Ajmal Mian . Adam Kortylewski, Bernhard Egger, Andreas Schneider, Thomas Gerig, Andreas Morel-Forster, Thomas Vetter . 78. DatasetJianglin Fu, Ivan V. Bajic, Rodney G. Vaughan . , Shu Liang, Ira Kemelmacher-Shlizerman, Linda G. Shapiro . Guang Hua, Han Liao, Haijian Zhang, Dengpan Ye, andJiayi Ma. Yan Zhuang, Shiying Li, Mohammad Shifat-E-Rabbi, Xuwang Yin, Abu Hasnat Mohammad Rubaiyat, Gustavo K. Rohde . z Mahdi Ghafourian, Julian Fierrez, Ruben Vera-Rodriguez, Ignacio Serna, Aythami Morales . I am unclear where your interpretation of fooling the discriminator about half the time comes from; my interpretation is that the convergence condition is when D(x)=1/2 rather than fooled half the time. Omid Abdollahi Aghdam, Behzad Bozorgtabar, Hazm Kemal Ekenel, Jean-Philippe Thiran . Jiayi Ma, Chengli Peng, Xin Tian, and Junjun Jiang. Thanks for sharing such a resourceful content. Milad Kiaee, Adam B Kashlak, Jisu Kim, Giseon Heo . 3DMM-CNNTran A T, Hassner T, Masi I, et al. 655-667, 2022.IF=8.182 Samil Karahan, Merve Kilinc Yildirim, Kadir Kirtac, Ferhat Sukru Rende, Gultekin Butun, Hazim Kemal Ekenel . Hitika Tiwari, Min-Hung Chen, Yi-Min Tsai, Hsien-Kai Kuo, Hung-Jen Chen, Kevin Jou, K. S. Venkatesh, Yong-Sheng Chen . Marija Ivanovska, Andrej Kronovek, Peter Peer, Vitomir truc, Borut Batagelj . , and corrupts it to a noisy version Norm-guided Adaptive Visual Embedding for Zero-Shot Sketch-Based Image Retrieval. Yaojie Liu, Joel Stehouwer, Amin Jourabloo, Xiaoming Liu . A Progressive Fusion Generative Adversarial Network for Realistic and Consistent Video Super-Resolution,IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(5), pp. Fatemeh Shiri, Xin Yu, Fatih Porikli, Richard Hartley, Piotr Koniusz . f Naser Damer, Jonas Henry Grebe, Cong Chen, Fadi Boutros, Florian Kirchbuchner, Arjan Kuijper . Assessment and Detection, A Smart Security System with Face Recognition, Deep Convolutional Neural Networks in the Face of Caricature: Identity and Image Revealed, Support Vector Guided Softmax Loss for Face Recognition, Nasal Patches and Curves for Expression-robust 3D Face Recognition, Face Recognition: A Novel Multi-Level Taxonomy based Survey, A Performance Comparison of Loss Functions for Deep Face Recognition, Open Source Face Recognition Performance Evaluation Package, SensitiveNets: Learning Agnostic Representations with Application to Face Recognition, Deep learning and face recognition: the state of the art, Cross-spectral Face Completion for NIR-VIS Heterogeneous Face Recognition, Max-C and Min-D Projection Autoassociative Fuzzy Morphological Memories: Theory and Applications for Face Recognition, Multi-Prototype Networks for Unconstrained Set-based Face Recognition, Face Recognition using Compressive Sensing, WIDER Face and Pedestrian Challenge 2018: Methods and Results, Video Face Recognition: Component-wise Feature Aggregation Network (C-FAN), Illumination-invariant Face recognition by fusing thermal and visual images via gradient transfer, BoostGAN for Occlusive Profile Face Frontalization and Recognition, Face Recognition Under Varying Blur, Illumination and Expression in an Unconstrained Environment, MassFace: an efficient implementation using triplet loss for face recognition, Occlusion-guided compact template learning for ensemble deep network-based pose-invariant face recognition, Fisher Discriminative Least Square Regression with Self-Adaptive Weighting for Face Recognition, Non-negative representation based discriminative dictionary learning for face recognition, Dual Variational Generation for Low-Shot Heterogeneous Face Recognition, Noise-Tolerant Paradigm for Training Face Recognition CNNs, Understanding Unconventional Preprocessors in Deep Convolutional Neural Networks for Face Identification. Moreover remember that log(ab)=log(a)+log(b)\log(a b)= \log(a) + \log(b)log(ab)=log(a)+log(b). Sakrapee Paisitkriangkrai, Chunhua Shen, Jian Zhang .
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