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[開學]106學年第1學期的課程看版開張了 歡迎同學問問題-20170917

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 於: 五月 12, 2019, 01:43:58 am 
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IEEE Transactions on Emerging Topics in Computational Intelligence
Special Issue on Adversarial Learning in Computational Intelligence

 於: 五月 12, 2019, 01:41:47 am 
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International Journal of Computer Vision Special Issue on
Generative Adversarial Networks for Computer Vision

Guest Editors


Jun-Yan Zhu, Massachusetts Institute of Technology
Hongsheng Li, The Chinese University of Hong Kong
Eli Shechtman, Adobe Research
Ming-Yu Liu, NVIDIA Research
Jan Kautz, NVIDIA Research
Antonio Torralba, Massachusetts Institute of Technology



Generative Adversarial Networks (GANs) have been at the forefront of research on generative models in the past few years. GANs can approximate real data distribution and synthesize realistic data samples. The concept of GANs is not limited to generating samples from certain data distributions but also has inspired many other research trends, including image generation and editing, feature learning, visual domain adaptation, data generation and augmentation for visual recognition, and many other practical applications, often leading to state of the art results. While GANs have achieved substantial progress for various computer vision applications, many issues remain to be solved and new research problems emerge. For example, what are the appropriate network structures and objective functions for generating visual data (e.g., images, videos, 3D)? What are the proper metrics for evaluating deep generative models? How can we improve the photorealism and resolution of the synthesized data samples? How can the generated data help solve other computer vision tasks?

This special issue provides a significant collective contribution to this emerging field of study. Specifically, we aim to solicit original contributions that include the following three areas:

Theoretical analysis and foundations: Authors are invited to submit manuscripts on the theoretical considerations of GANs and its variants such as the convergence and the limitations of models.
Novel formulations and training methods: We would like to solicit submissions on new network architectures, robust objective functions, and better training procedures that can improve the quality, resolution, and training stability of GANs-based models.
New computer vision applications: We welcome new work that explores GANs-based approaches for computer vision applications. We encourage original research in these fields to discuss how they adopt adversarial learning to individual computer vision applications. Besides, we also encourage submissions on solving cross-disciplinary research problems through adversarial learning, such as vision and language as well as robotics and vision

 於: 五月 12, 2019, 01:39:24 am 
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30 September 2019 - Submission deadline
31 December 2019 - First decision notification
28 February 2020 - Revised version deadline
30 April 2020 - Final decision notification
July 2020 - Publication

 於: 五月 12, 2019, 01:37:33 am 
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Abstract submission deadline: February 19, 2019 (11:59PM UTC-12)
Paper submission deadline: February 25, 2019 (11:59PM UTC-12)
Rebuttal period: April 15, 0:00 UTC-12 - April 20 23:59 UTC-12
Paper notification: May 9, 23:59 UTC -12

 於: 三月 07, 2019, 12:41:59 am 
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 於: 二月 13, 2019, 02:00:28 pm 
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Ubuntu 16.04 安裝 TensorFlow GPU GTX 1060
程式碼: [Select]
sudo dpkg -i cuda-repo-ubuntu1604-9-0-local_9.0.176-1_amd64.deb
sudo apt-key add /var/cuda-repo-<version>/
sudo apt-get update

verify CUDA installation in 16.04
程式碼: [Select]
nvcc --version

 於: 二月 12, 2019, 11:34:38 pm 
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Paper submission due: May 9, 2019

Notification of acceptance: June 21, 2019

Registration due: July 20, 2019

Final manuscript due: August 1, 2019

 於: 二月 05, 2019, 12:15:45 am 
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 於: 一月 27, 2019, 01:46:21 am 
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 於: 一月 27, 2019, 01:14:56 am 
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  • 論文投稿方式採線上投稿,投稿網址及論文格式說明請參閱

會議地點:中臺科技大學 勤學樓 B1 國際會議廳
聯絡人:中臺科技大學資訊管理系 謝嘉芳小姐

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