Sujay Yadawadkar, Virginia Tech

Slides:

Advertisements

Similar presentations

Face Recognition: A Convolutional Neural Network Approach

Advertisements

Novell Server Linux vs. windows server 2008 By: Gabe Miller.

Presenting: Itai Avron Supervisor: Chen Koren Final Presentation Spring 2005 Implementation of Artificial Intelligence System on FPGA.

Desktop with Direct3D 10 capable hardware Laptop with Direct3D 10 capable hardware Direct3D 9 capable hardware Older or no graphics hardware.

Digital Graphics and Computers. Hardware and Software Working with graphic images requires suitable hardware and software to produce the best results.

Kamalapurkar Shounak Rajarshi Salil Joshi Rohan Bhavsar Sagar Pai Sandesh Low Latency Publisher-Subscriber Network for Stock Market Application Team WhiteWalkers.

Creating With Code.

Applying GPU and POSIX Thread Technologies in Massive Remote Sensing Image Data Processing By: Group 17 King Mongkut's Institute of Technology Ladkrabang.

Hardware. Make sure you have paper and pen to hand as you will need to take notes and write down answers and thoughts that you can refer to later on.

Canny Edge Detection Using an NVIDIA GPU and CUDA Alex Wade CAP6938 Final Project.

Convolutional Neural Network

Unit 2 Assignment 2 M2 RECOMMENDED PC. ZOOSTORM Processor – Intel Core i Operating system – Windows 8 Memory – 8GB RAM Hard Drive – 1TB.

Convolutional Neural Networks for Direct Text Deblurring

NOTE: To change the image on this slide, select the picture and delete it. Then click the Pictures icon in the placeholder to insert your own image. P.

Microsoft Educator Network Free Tool: WorldWide Telescope (all the way at the bottom) 1b-d f85-27f369ee76f9.

NOTE: To change the image on this slide, select the picture and delete it. Then click the Pictures icon in the placeholder to insert your own image. P.

 Input - A device, such as a keyboard, used to enter information into a computer  Output - Electronic or electromechanical equipment connected to.

Hardware refers to the tangible parts of computer systems and typically includes support for processing, storage, input, and output. Hardware Processing.

Conditional Generative Adversarial Networks

Configure the intercom IP

TensorFlow CS 5665 F16 practicum Karun Joseph, A Reference:

Generalized and Hybrid Fast-ICA Implementation using GPU

Analysis of Sparse Convolutional Neural Networks

Under the Guidance of V.Rajashekhar M.Tech Assistant Professor

Convolutional Neural Network

Image Transformation 4/30/2009

Heterogeneous Computation Team HybriLIT

ECE 5424: Introduction to Machine Learning

Perceptual Loss Deep Feature Interpolation for Image Content Changes

Image Recognition. Contents: Motivation Objective Definition Introduction Preprocessing / Edge Detection Neural Networks in Image Recognition Practical.

Action Recognition Experiments

Classification of Hand-Written Digits Using Scattering Convolutional Network Dongmian Zou Advisor: Professor Radu Balan.

A VERY Brief Introduction to Convolutional Neural Network using TensorFlow 李弘

הכרת המחשב האישי PC - Personal Computer

How to Produce Music By: Ryko Nez Knuckle Baller - Intro.

Authors: Jun-Yan Zhu*, Taesun Park*, Phillip Isola, Alexei A. Efros

ISAM 5338 Project Business Plan

Introduction to CuDNN (CUDA Deep Neural Nets)

Neural Networks and Backpropagation

Neural Photo Editing Andrew Brock.

Layer-wise Performance Bottleneck Analysis of Deep Neural Networks

Bird-species Recognition Using Convolutional Neural Network

Convolutional Neural Networks

Deep Learning Packages

Jia-Bin Huang Virginia Tech ECE 6554 Advanced Computer Vision

Counting in Dense Crowds using Deep Learning

Interaction of Hardware

Presented by Shixing Chen

GPU Accelerated Image Super-Resolution

Non-Stationary Texture Synthesis by Adversarial Expansion

Transp Course 2014 Overview.

Image to Image Translation using GANs

GAN Applications.

Hardware Accelerated Video Decoding in

Overview of Neural Network Architecture Assignment Code

Machine Learning – Neural Networks David Fenyő

Machine Learning at the Edge

Artificial Neural Networks

Face Recognition: A Convolutional Neural Network Approach

TPGAN overview.

Internal components of a computer.

AI for Beginners from a Beginner

Deep screen image crop and enhance

Deep screen image crop and enhance

Deep screen image crop and enhance

Recent Developments on Super-Resolution

CRCV REU 2019 Aaron Honculada.

Deep screen image crop and enhance

Presentation transcript:

Sujay Yadawadkar, Virginia Tech Style Transfer Sujay Yadawadkar, Virginia Tech

Agenda: Style transfer: Quick overview Real time performance Experiment Results

Overview: Picture Credit: Image style transfer using convolutional neural networks. L. A. Gatys, A. S. Ecker, M. Bethge, CVPR 2016

Limitation: Gradient descent for every image. Can we build a faster style transformation network ..?

Real Time Style Transfer Perceptual Losses for Real-Time Style Transfer and Super-Resolution. J. Johnson, A. Alahi, L. Fei- Fei, ECCV

Experiment: Hardware: PC with Intel Core-i7 5th Gen. 8gb ram No GPU used. Software Dependencies: Torch Cuda (optional, not used) Cudnn (optional, not used) Procedure: Use pretrained torch model.

Results: Input Image Output Style Model

Thank you!