PPT-Generative adversarial networks (GANs)

for edge detection Z Zeng YK Yu KH Wong In IEEE iciev2018 International Conference on Informatics Electronics amp Vision Junekitakyushu exhibition center japan 2529 2018 . Goodfellow Jean PougetAbadie Mehdi Mirza Bing Xu David WardeFarley Sherjil Ozair Aaron Courville Yoshua Bengio epartement dinformatique et de recherche op erationnelle Universit e de Montr eal Montr eal QC H3C 3J7 Abstract We propose a etc. Convnets. (optimize weights to predict bus). bus. Convnets. (optimize input to predict ostrich). ostrich. Work on Adversarial examples by . Goodfellow. et al. , . Szegedy. et. al., etc.. Generative Adversarial Networks (GAN) [. Statistical Relational AI. Daniel Lowd. University of Oregon. Outline. Why do we need adversarial modeling?. Because of the dream of AI. Because of current reality. Because of possible dangers. Our initial approach and results. Nets. İlke Çuğu 1881739. NIPS 2014 . Ian. . Goodfellow. et al.. At a . glance. (. http://www.kdnuggets.com/2017/01/generative-adversarial-networks-hot-topic-machine-learning.html. ). Idea. . Behind. Adversarial examples. Ostrich!. Adversarial examples. Ostrich!. Intriguing properties of neural networks. . Christian . Szegedy. , . Wojciech. . Zaremba. , Ilya . Sutskever. , Joan Bruna, . Dumitru. Presenters: Pooja Harekoppa, Daniel Friedman. Explaining and Harnessing Adversarial Examples. Ian J. . Goodfellow. , Jonathon . Shlens. and Christian . Szegedy. Google Inc., Mountain View, CA. Highlights . Generative Adversarial Networks. Mike . Mozer. Department of Computer Science and. Institute of Cognitive Science. University of Colorado at Boulder. From . statsmemes. @ . facebook. (Thanks . Adi. !). ML Reading . Group. Xiao Lin. Jul. 22 2015. I. . Goodfellow. , J. . Pouget-Abadie. , M. Mirza, B. Xu, D. . Warde. -Farley, S. . Ozair. , A. . Courville. and Y. . Bengio. . . "Generative adversarial nets." . Erdős-Rényi. Random model, . Watts-. Strogatz. Small-world, . Barabási. -Albert Preferential attachment, . Molloy-Reed . Configuration model . and . Gilbert . Random . G. eometric model. Excellence Through Knowledge. Akrit Mohapatra. ECE Department, Virginia Tech. What are GANs?. System of . two neural networks competing against each other in a zero-sum game framework. . They were first introduced by . Ian Goodfellow. ). Prof. . Ralucca Gera, . Applied Mathematics Dept.. Naval Postgraduate School. Monterey, California. rgera@nps.edu. Excellence Through Knowledge. Learning Outcomes. I. dentify . network models and explain their structures. The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand Dr. Alex Vakanski. Lecture 6. GANs for Adversarial Machine Learning. Lecture Outline. Mohamed Hassan presentation. Introduction to Generative Adversarial Networks (GANs). Jeffrey Wyrick presentation. Generative Adversarial Networks (GANs). Generative Adversarial Networks (GANs). Goodfellow. et al (2014) . https://arxiv.org/abs/1406.2661. Minimize distance between the distributions of real data and generated samples.