# Fully Convolutional Fractional Scaling

arXiv: https://arxiv.org/abs/2203.10670 · HTML: https://mlsanity.com/paper/2203.10670/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 28 references that are in the map.
- First posted on arXiv 20 Mar 2022.

## Builds on (most cited first, 18 of 18)

- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://mlsanity.com/paper/1703.05921/) (2017)
- ["Zero-Shot" Super-Resolution using Deep Internal Learning](https://mlsanity.com/paper/1712.06087/) (2017)
- [Residual Dense Network for Image Restoration](https://mlsanity.com/paper/1812.10477/) (2018)
- [Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks](https://mlsanity.com/paper/2107.07933/) (2021)
- [Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial Networks](https://mlsanity.com/paper/1809.00437/) (2018)
- [Unpaired Image Super-Resolution using Pseudo-Supervision](https://mlsanity.com/paper/2002.11397/) (2020)
- [UPFlow: Upsampling Pyramid for Unsupervised Optical Flow Learning](https://mlsanity.com/paper/2012.00212/) (2020)
- [Video Salient Object Detection via Fully Convolutional Networks](https://mlsanity.com/paper/1702.00871/) (2017)
- [Instance-sensitive Fully Convolutional Networks](https://mlsanity.com/paper/1603.08678/) (2016)
- [Fractional Max-Pooling](https://mlsanity.com/paper/1412.6071/) (2014)
- [Upsampling artifacts in neural audio synthesis](https://mlsanity.com/paper/2010.14356/) (2020)
- [Detail-Preserving Pooling in Deep Networks](https://mlsanity.com/paper/1804.04076/) (2018)
- [S3Pool: Pooling with Stochastic Spatial Sampling](https://mlsanity.com/paper/1611.05138/) (2016)
- [Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network](https://mlsanity.com/paper/1707.05425/) (2017)
- [The Analysis of Projective Transformation Algorithms for Image Recognition on Mobile Devices](https://mlsanity.com/paper/1912.01401/) (2019)
- [Convolutional Block Design for Learned Fractional Downsampling](https://mlsanity.com/paper/2105.09999/) (2021)

## 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.