DL之DilatedConvolutions：Dilated Convolutions(膨脹卷積/擴張卷積)算法的簡介(論文介紹)、架構詳解、案例應用等配圖集合之詳細攻略

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目錄

Dilated Convolutions算法的簡介(論文介紹)

Dilated Convolutions算法的架構詳解

1、膨脹卷積的應用——語義分割網絡中引入膨脹卷積

2、膨脹卷積的優點

3、卷積、反卷積與膨脹卷積

Dilated Convolutions算法的案例應用

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Dilated Convolutions算法的簡介(論文介紹)

ABSTRACT ?
? ? ? State-of-the-art models for semantic segmentation are based on adaptations of ?convolutional networks that had originally been designed for image classification. ?However, dense prediction problems such as semantic segmentation are ?structurally different from image classification. In this work, we develop a new ?convolutional network module that is specifically designed for dense prediction. ?The presented module uses dilated convolutions to systematically aggregate multiscale ?contextual information without losing resolution. The architecture is based ?on the fact that dilated convolutions support exponential expansion of the receptive ?field without loss of resolution or coverage. We show that the presented context ?module increases the accuracy of state-of-the-art semantic segmentation systems. ?In addition, we examine the adaptation of image classification networks to dense ?prediction and show that simplifying the adapted network can increase accuracy.
? ? ? 最先進的語義分割模型是基于卷積網絡的自適應，而卷積網絡最初是為圖像分類而設計的。然而，語義分割等密集預測問題在結構上與圖像分類不同。在這項工作中，我們開發了一個新的卷積網絡模塊，專門為密集預測設計。所提出的模組使用擴展卷積來系統地聚合多尺度的上下文信息而不丟失分辨率。該架構基于這樣一個事實，即膨脹的卷積支持接收域的指數級擴展，而不會丟失分辨率或覆蓋率。結果表明，提出的上下文模塊提高了目前最先進的語義分割系統的精度。此外，我們研究了圖像分類網絡對密集預測的適應性，并證明簡化自適應網絡可以提高精度。
CONCLUSION ?
? ? ? We have examined convolutional network architectures for dense prediction. Since the model must ?produce high-resolution output, we believe that high-resolution operation throughout the network is both feasible and desirable. Our work shows that the dilated convolution operator is particularly ?suited to dense prediction due to its ability to expand the receptive field without losing resolution ?or coverage. We have utilized dilated convolutions to design a new network structure that reliably ?increases accuracy when plugged into existing semantic segmentation systems. As part of this work, ?we have also shown that the accuracy of existing convolutional networks for semantic segmentation ?can be increased by removing vestigial components that had been developed for image classification.
? ? ? 我們研究了用于密集預測的卷積網絡架構。由于模型必須產生高分辨率的輸出，我們認為整個網絡的高分辨率操作是可行的，也是可取的。我們的工作表明，膨脹卷積算子特別適合于密集預測，因為它能夠在不損失分辨率或覆蓋率的情況下擴展接收域。我們利用擴展卷積設計了一種新的網絡結構，當插入現有的語義分割系統時，可以可靠地提高精確度。作為這項工作的一部分，我們還表明，通過去除用于圖像分類的殘留成分，可以提高現有卷積網絡用于語義分割的準確性。
? ? ? We believe that the presented work is a step towards dedicated architectures for dense prediction that ?are not constrained by image classification precursors. As new sources of data become available, ?future architectures may be trained densely end-to-end, removing the need for pre-training on image ?classification datasets. This may enable architectural simplification and unification. Specifically, ?end-to-end dense training may enable a fully dense architecture akin to the presented context network ?to operate at full resolution throughout, accepting the raw image as input and producing dense ?label assignments at full resolution as output. ?
? ? ? 我們認為，所提出的工作是朝著不受圖像分類前驅體約束的高密度預測專用體系結構邁進的一步。隨著新數據源的出現，未來的體系結構可能需要密集的端到端培訓，從而無需對圖像分類數據集進行預培訓。這可能使架構簡化和統一成為可能。具體地說，端到端密集訓練可能使類似于所述上下文網絡的完全密集的體系結構能夠以全分辨率運行，接受原始圖像作為輸入，并以全分辨率生成密集的標簽分配作為輸出。
? ? ? State-of-the-art systems for semantic segmentation leave significant room for future advances. Failure ?cases of our most accurate configuration are shown in Figure 4. We will release our code and ?trained models to support progress in this area.
? ? ? 最先進的語義分割系統為未來的發展留下了巨大的空間。圖4顯示了我們最精確配置的故障案例。我們將發布我們的代碼和經過培訓的模型來支持這一領域的進展。

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論文
Fisher Yu, VladlenKoltun.
Multi-Scale Context Aggregation by Dilated Convolutions. ICLR, 2016
https://arxiv.org/abs/1511.07122

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Dilated Convolutions算法的架構詳解

更新……

1、卷積、反卷積與膨脹卷積

DL之CNN：卷積神經網絡算法簡介之卷積矩陣、轉置卷積(反卷積Transpose)、膨脹卷積(擴張卷積Dilated)、帶孔卷積(atrous )之詳細攻略

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Dilated Convolutions算法的案例應用

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