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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/124375


    Title: 基於合作學習的神經網路進行圖片轉換
    Image-to-Image Translation with Cooperative Learning Networks
    Authors: 翁健豪
    Weng, Chien-Hao
    黃存宇
    郁方
    Yu, Fang
    Contributors: 2019智慧企業資訊應用發展國際研討會
    Keywords: 生成式合作網路;圖像轉換;深度學習;神經網路
    Cooperative learning networks;Image-to-Image Translation;deep learning;neural network
    Date: 2019-06
    Issue Date: 2019-07-17 15:16:02 (UTC+8)
    Abstract: 本論文提出了一個新的圖像轉換方法，主要的功能是能實現將某一組被特定標籤後的資料還原近似原始資料的樣子。例如我們將一張真實照片做成一張標籤化的圖片，透過大量資料集的訓練，我們可以讓我們的神經網路學習到標籤化的圖片該如何還原成原來的真實照片，在本文中的實驗部分我們會看到這樣的結果。在之前的論文中已經有人提出對於這個問題的解法，最著名的是pix2pix，此作者使用了conditional-GAN的概念來解決此問題。在我們所使用的方法中，我們提出了一種新的方法，不同於GAN的生成式對抗網路，我們採用生成式合作網路的概念，在本文中我們將這方法命名為Coop_pix2pix。生成式合作網路在之前的作品中，最著名的是CoopNet，我們的作法也是由此出發，將CoopNet中合作網路的概念拿來解圖片轉換的題目，我們可以在後面的章節看到使用這方法產生的圖片所帶來的效果。
    This paper proposes a new Image-to-Image translation method. Our work is to restore the original picture from the picture with specific labels as close as possible. For example, we label a real photo to a labeled image. After training by a large number of data sets, we can let our neural network learn how to convert the labeled image to the original photo. We will show the result in the experiment part. There are several previous works solving this problem. For example, pix2pix is a famous work, which uses the concept of conditional-GAN to solve this problem. In our works, we propose a new method. Different from GAN`s generative adversarial network, we use concept of the cooperative learning network. We name this method Coop_pix2pix. CoopNet is a famous example in previous works of the cooperative learning network. Our work based on the cooperative neural networks concept, using this concept to solve Image-to-Image translation problem. We will demonstrate our work’s performance in our experiments part.
    Relation: 2019智慧企業資訊應用發展國際研討會
    Data Type: conference
    Appears in Collections:[2019智慧企業資訊應用發展國際研討會] 會議論文

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