PPT-Assignment 4: Deep Convolutional Neural Networks

cs543 ece549 Spring 2016 Due date Wednesday May 4 115959PM Prepared with the help of ChihHui Ho Platform Kaggle in class Create an account Click on invitation Then you will be added . using Convolutional Neural Network and Simple Logistic Classifier. Hurieh. . Khalajzadeh. Mohammad . Mansouri. Mohammad . Teshnehlab. Table of Contents. Convolutional Neural . Networks. Proposed CNN structure for face recognition. Kong Da, Xueyu Lei & Paul McKay. Digit Recognition. Convolutional Neural Network. Inspired by the visual cortex. Our example: Handwritten digit recognition. Reference: . LeCun. et al. . Back propagation Applied to Handwritten Zip Code Recognition. Deep Learning @ . UvA. UVA Deep Learning COURSE - Efstratios Gavves & Max Welling. LEARNING WITH NEURAL NETWORKS . - . PAGE . 1. Machine Learning Paradigm for Neural Networks. The Backpropagation algorithm for learning with a neural network. Sergey Zagoruyko & Nikos Komodakis. Introduction. Comparing Patches across images is one of the most fundamental tasks in computer vision. Applications include structure from motion, wide baseline matching and building panorama. Sabareesh Ganapathy. Manav Garg. Prasanna. . Venkatesh. Srinivasan. Convolutional Neural Network. State of the art in Image classification. Terminology – Feature Maps, Weights. Layers - Convolution, . By, . . Sruthi. . Moola. Convolution. . Convolution is a common image processing technique that changes the intensities of a pixel to reflect the intensities of the surrounding pixels. A common use of convolution is to create image filters. Abhinav . Podili. , Chi Zhang, Viktor . Prasanna. Ming Hsieh Department of Electrical Engineering. University of Southern California. {. podili. , zhan527, . prasanna. }@usc.edu. fpga.usc.edu. ASAP, July 2017. 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”. Introduction 2. Mike . Mozer. Department of Computer Science and. Institute of Cognitive Science. University of Colorado at Boulder. Hinton’s Brief History of Machine Learning. What was hot in 1987?. Ali Cole. Charly. . Mccown. Madison . Kutchey. Xavier . henes. Definition. A directed network based on the structure of connections within an organism's brain. Many inputs and only a couple outputs. Article and Work by. : Justin . Salamon. and Juan Pablo Bello. Presented by . : . Dhara. Rana. Overall Goal of Paper. Create a way to classify environmental sound given an audio clip. Other methods of sound classification: (1) dictionary learning and (2) wavelet filter banks . Dr David Wong. (With thanks to Dr Gari Clifford, G.I.T). The Multi-Layer Perceptron. single layer can only deal with linearly separable data. Composed of many connected neurons . Three general layers; . José Ignacio Orlando. 1,2. , Elena Prokofyeva. 3,4. , Mariana del Fresno. 1,5. and Matthew B. Blaschko. 6. 1 . Instituto. . Pladema. , UNCPBA, . Tandil. , Argentina. 2. . Consejo. Nacional de . Investigaciones. An overview and applications. Outline. Overview of Convolutional Neural Networks. The Convolution operation. A typical CNN model architecture. Properties of CNN models. Applications of CNN models. Notable CNN models.