PPT-Deep Residual Learning for Image

Recognition Author Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun accepted to CVPR 2016 Presenter Hyeongseok Son The deeper the better The deeper network can cover more complex problems. Naiyan. Wang. Outline. Non-NN Approaches. Deep Convex Net. Extreme Learning Machine. PCAnet. Deep Fisher Net (Already . presented before). Discussion. Deep convex net. Each module is a two- layer convex network.. Carey . Nachenberg. Deep Learning for Dummies (Like me) – Carey . Nachenberg. (Like me). The Goal of this Talk?. Deep Learning for Dummies (Like me) – Carey . Nachenberg. 2. To provide you with . Presenter: . Yanming. . Guo. Adviser: Dr. Michael S. Lew. Deep learning. Human. Computer. 1:4. Human . v.s. . Computer. Deep learning. Human. Computer. 1:4. Human . v.s. . Computer. Deep Learning. Why better?. Wenchi. Ma. Computer Vision Group . EECS,KU. Inception: From NIN to . Googlenet. m. icro network. A general . nonlinear. function . approximator. Enhance the abstraction ability of the local model. The Future of Real-Time Rendering?. 1. Deep Learning is Changing the Way We Do Graphics. [Chaitanya17]. [Dahm17]. [Laine17]. [Holden17]. [Karras17]. [Nalbach17]. Video. “. Audio-Driven Facial Animation by Joint End-to-End Learning of Pose and Emotion”. CS 501:CS Seminar. Min Xian. Assistant Professor. Department of Computer Science. University of Idaho. Image from NVIDIA. Researchers:. Geoff Hinton. Yann . LeCun. Andrew Ng. Yoshua. . Bengio. …. Presenter : Jingyun Ning. “CVPR 2016 Best Paper Award”. Introduction. Deep Residual Networks (ResNets). A simple and clean framework of training “very” deep nets. State-of-the-art performance for. Deep Learning for Expression Recognition in Image Sequences Daniel Natanael García Zapata Tutors: Dr. Sergio Escalera Dr. Gholamreza Anbarjafari April 27 2018 Introduction and Goals Introduction Dennis Hamester et al., “Face ExpressionRecognition with a 2-Channel ConvolutionalNeural Network”, International Joint Conference on Neural Networks (IJCNN), 2015. the leading supplier of machine vision vision and image-based barcode reading technology Deployed by the world146s top manufacturers suppliers requirements of each industryCognex solutions help custo School . of Management. New Jersey Institute Of Technology. U. ncontrolled growth of abnormal skin cells. Often caused by ultraviolet radiation from sunshine or tanning beds. Potential Genetic basis for susceptibility. Commercially . available seizure detection systems suffer from unacceptably high false alarm rates. . Deep . learning algorithms, like Convolutional Neural Networks (CNNs), have not previously been effective due to the lack of big data resources. . mentor:. . wei. . yang. mentee:. . Ximin. . lin. Deep Neural Networks. Deep Neural Networks. It is possible to fool the deep-learning system . Preliminary study - Identify characters in the image. Topics: 1. st. lecture wrap-up, difficulty training deep networks,. image classification problem, using convolutions,. tricks to train deep networks . . Resources: http://www.cs.utah.edu/~rajeev/cs7960/notes/ . Subset of the publicly available TUH EEG Corpus (. www.isip.piconepress.com/projects/tuh_eeg). .. Evaluation Data:. 50 patients, 239 sessions, 1015 files. 171 hours of data including 16 hours of seizures..