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Fully Convolutional Fractional Scaling

arXiv 2203.10670 ↗First posted 20 Mar 2022cs.NE cs.LGcited by 0

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Abstract (arXiv)

We introduce a fully convolutional fractional scaling component, FCFS. Fully convolutional networks can be applied to any size input and previously did not support non-integer scaling. Our architecture is simple with an efficient single layer implementation. Examples and code implementations of three common scaling methods are published.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 28 references that are in the map. First posted on arXiv 20 Mar 2022.

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18 of 28 references.

References · 18

  1. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  2. 2016Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Networkcited by 1,199
  3. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  4. 2017"Zero-Shot" Super-Resolution using Deep Internal Learningcited by 285
  5. 2018Residual Dense Network for Image Restorationcited by 181
  6. 2021Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networkscited by 141
  7. 2018Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial Networkscited by 133
  8. 2020Unpaired Image Super-Resolution using Pseudo-Supervisioncited by 67
  9. 2020UPFlow: Upsampling Pyramid for Unsupervised Optical Flow Learningcited by 49
  10. 2017Video Salient Object Detection via Fully Convolutional Networkscited by 46
  11. 2016Instance-sensitive Fully Convolutional Networkscited by 36
  12. 2014Fractional Max-Poolingcited by 35
6 more
  1. 2020Upsampling artifacts in neural audio synthesiscited by 28
  2. 2018Detail-Preserving Pooling in Deep Networkscited by 26
  3. 2016S3Pool: Pooling with Stochastic Spatial Samplingcited by 17
  4. 2017Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Networkcited by 17
  5. 2019The Analysis of Projective Transformation Algorithms for Image Recognition on Mobile Devicescited by 4
  6. 2021Convolutional Block Design for Learned Fractional Downsamplingcited by 3

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