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      Partial convolution for padding, inpainting, and image synthesis

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      Author(s)
      Liu, Guilin
      Dündar, Ayşegül
      Shih, Kevin J.
      Wang, Ting-Chun
      Reda, Fitsum A.
      Sapra, Karan
      Yu, Zhiding
      Yang, Xiaodong
      Tao, Andrew
      Catanzaro, Bryan
      Date
      2022-09-26
      Source Title
      IEEE Transactions on Pattern Analysis and Machine Intelligence
      Print ISSN
      0162-8828
      Electronic ISSN
      1939-3539
      Publisher
      IEEE
      Pages
      1 - 15
      Language
      English
      Type
      Article
      Item Usage Stats
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      40
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      Abstract
      Partial convolution weights convolutions with binary masks and renormalizes on valid pixels. It was originally proposed for image inpainting task because a corrupted image processed by a standard convolutional often leads to artifacts. Therefore, binary masks are constructed that define the valid and corrupted pixels, so that partial convolution results are only calculated based on valid pixels. It has been also used for conditional image synthesis task, so that when a scene is generated, convolution results of an instance depend only on the feature values that belong to the same instance. One of the unexplored applications for partial convolution is padding which is a critical component of modern convolutional networks. Common padding schemes make strong assumptions about how the padded data should be extrapolated. We show that these padding schemes impair model accuracy, whereas partial convolution based padding provides consistent improvements across a range of tasks. In this paper, we review partial convolution applications under one framework. We conduct a comprehensive study of the partial convolution based padding on a variety of computer vision tasks, including image classification, 3D-convolution-based action recognition, and semantic segmentation. Our results suggest that partial convolution-based padding shows promising improvements over strong baselines.
      Keywords
      Image inpainting
      Image synthesis
      Object classification
      Padding
      Partial convolution
      Semantic segmentation
      Permalink
      http://hdl.handle.net/11693/111449
      Published Version (Please cite this version)
      https://www.doi.org/10.1109/TPAMI.2022.3209702
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