The Unreasonable Effectiveness of Deep Features as a Perceptual Metric 文章解讀
文章目錄 文章翻譯 文章略讀 文章精讀 答疑解惑 文章翻譯 文章略讀 圖1： 這些樣例中左邊還是右邊的圖像塊和中間圖像塊最接近呢？在每一個例子中，一些傳統相似性度量方法(L2/PSNR，SSIM，FSIM)得出的結論和人類判斷的不一致。但是深度學習的方法沒從不同的結構(Squeezenet，AlexNet，VGG)和不同的監督類型(監督方法，半監督方法，甚至無監督方法)，都和人類的認知很好的吻合。
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- 1. The Unreasonable Effectiveness of Recurrent Neural Networks
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相關文章
- 1. The Unreasonable Effectiveness of Recurrent Neural Networks
- 2. CVPR 論文閱讀1：圖像檢索、深度感知測量方法：The Unreasonable Effectiveness of Deep Features as a Perceptual Metric-2018
- 3. [論文解讀]Comparing the Effectiveness of Testing Techniques
- 4. [解讀] Deep Clustering for Unsupervised Learning of Visual Features
- 5. CV：翻譯並解讀2019《A Survey of the Recent Architectures of Deep Convolutional Neural Networks》第一章~第三章
- 6. CV：翻譯並解讀2019《A Survey of the Recent Architectures of Deep Convolutional Neural Networks》
- 7. [評價指標]LPIPS論文解讀
- 8. CV：翻譯並解讀2019《A Survey of the Recent Architectures of Deep Convolutional Neural Networks》第四章
- 9. hive-the summaries of features
- 10. [文獻閱讀筆記] Assessing the Effectiveness of Domain Blacklisting Against Malicious DNS Registrations