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Image-to-Image Translation with Conditional Adversarial Networks Phillip Isola, Jun-Yan Zhu, Tinghui Zhou and Alexei A. Efros Berkeley AI Research (BAIR) Laboratory, UC Berkeley ECS 289G: Visual Recognition Presenters: Terry Yang and Mingyang Zhou Agenda ● Motivating the problem ● Introduction to GANs ● Method ● Dataset & tasks ● Evaluation Metrics ● Results ● Strength & Weakness ● Future Extension Introduction: Image-to-Image Translation Isola, Phillip, et al. "Image-To-Image Translation With Conditional Adversarial Networks." CVPR, 2017. Previous Methods T. Chen, M.-M. Cheng, P. Tan, A. A. Buades, B. Coll, and J.-M. R. Zhang, P. Isola, and A. A. Shamir, and S.-M. Hu. Morel. A non-local algorithm for Efros. Colorful image Sketch2photo: internet image image denoising. CVPR, 2015. colorization. ECCV, 2016. montage. ACM, 2009 Required loss functions and architectures designed specifically for the task at hand. Motivation Goal: A general-purpose solution to image-to-image translation problems. Garcia, Victor. "Image-to-Image This paper: Use the same architecture and objective Translation with Conditional Adversarial Networks." 25 Nov 2016. for each image-to-image translation task. UPC Computer Vision Reading Group, Universitat Politècnica de Catalunya, Microsoft Powerpoint presentation Quick Overview of Generative Adversarial Networks ©2014-2016 Ociacia Generative Adversarial Networks (GANs) Garcia, Victor. "Image-to-Image Translation with Conditional Adversarial Networks." 25 Nov 2016. UPC Computer Vision Reading Group, Universitat Politècnica de Catalunya, Microsoft Powerpoint presentation Conditional Generative Adversarial Networks (cGANs) Garcia, Victor. "Image-to-Image Translation with Conditional Adversarial Networks." 25 Nov 2016. UPC Computer Vision Reading Group, Universitat Politècnica de Catalunya, Microsoft Powerpoint presentation Objective Isola, Phillip, et al. "Image-To-Image Translation With Conditional Adversarial Networks." CVPR, 2017. Generator Encoder-Decoder U-Net Network Garcia, Victor. "Image-to-Image Translation with Conditional Adversarial Networks." 25 Nov 2016. UPC Computer Vision Reading Group, Universitat Politècnica de Catalunya, Microsoft Powerpoint presentation

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Alternate between one gradient descent step on D and one gradient descent step on G. ○ Minibatch SGD and Adam optimization. ○ At inference time, the generator net is run in exactly the same manner as during training phase. Isola, Phillip, et al. "Image-To-Image Translation. With Conditional
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Alternate between one gradient descent step on D and one gradient descent step on G. ○ Minibatch SGD and Adam optimization. ○ At inference time, the generator net is run in exactly the same manner as during training phase. Isola, Phillip, et al. "Image-To-Image Translation. With Conditional

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