PPT-Deep Learning for Vision

Author : briana-ranney | Published Date : 2015-09-20

Adam Coates Stanford University Visiting Scholar Indiana University Bloomington What do we want ML to do Given image predict complex highlevel patterns Object recognition

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Deep Learning for Vision: Transcript

Adam Coates Stanford University Visiting Scholar Indiana University Bloomington What do we want ML to do Given image predict complex highlevel patterns Object recognition Detection Segmentation. Quoc V. Le. Stanford University and Google. Purely supervised. Quoc V. . Le. Almost abandoned between 2000-2006. - . Overfitting. , slow, many local minima, gradient vanishing. In 2006, Hinton, et. al. proposed RBMs to . Information Processing & Artificial Intelligence. New-Generation Models & Methodology for Advancing . AI & SIP. Li Deng . Microsoft Research, Redmond, . USA. Tianjin University, July 4, 2013 (Day 3). Aaron Crandall, 2015. What is Deep Learning?. Architectures with more mathematical . transformations from source to target. Sparse representations. Stacking based learning . approaches. Mor. e focus on handling unlabeled data. Professor Qiang Yang. Outline. Introduction. Supervised Learning. Convolutional Neural Network. Sequence Modelling: RNN and its extensions. Unsupervised Learning. Autoencoder. Stacked . Denoising. . Pierre . Baldi. University of California, Irvine. Two Questions. “If we solve computer vision, we have pretty much solved AI.” . A-NNs . vs. B-NNs and Deep Learning.. If we solve computer vision…. 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?. Continuous. Scoring in Practical Applications. Tuesday 6/28/2016. By Greg Makowski. Greg@Ligadata.com. www.Linkedin.com/in/GregMakowski. Community @. . http. ://. Kamanja.org. . . Try out. Future . 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”. Garima Lalwani Karan Ganju Unnat Jain. Today’s takeaways. Bonus RL recap. Functional Approximation. Deep Q Network. Double Deep Q Network. Dueling Networks. Recurrent DQN. Solving “Doom”. The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand Outline. What is Deep Learning. Tensors: Data Structures for Deep Learning. Multilayer Perceptron. Activation Functions for Deep Learning. Model Training in Deep Learning. Regularization for Deep Learning. About the class. COMP 648: Computer Vision Seminar. Instructor: . Vicente. . Ordóñez. (Vicente . Ordóñez. Román). Website: . https://www.cs.rice.edu/~vo9/cv-seminar. Location: Zoom – Keck Hall 101. Manoranjan . Paul. , PhD, SMIEEE, MACS (Snr) CP. Associate Professor in Computer Science. School . of Computing & . Mathematics, Faculty of BJBS. Steering Committee Member. CSU Machine Learning (CML) Research Unit. Transfer Learning. Transfer a model trained on . source. data A to . target . data B. Task transfer: . in this case, . the source and target data can be the same. Image classification -> image segmentation.

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