# Dual Pipeline Style Transfer with Input Distribution Differentiation

arXiv: https://arxiv.org/abs/2311.05432 · HTML: https://mlsanity.com/paper/2311.05432/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 32 of its 42 references that are in the map.
- First posted on arXiv 9 Nov 2023.

## Builds on (most cited first, 30 of 32)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)
- [A Learned Representation For Artistic Style](https://mlsanity.com/paper/1610.07629/) (2016)
- [Going Deeper in Spiking Neural Networks: VGG and Residual Architectures](https://mlsanity.com/paper/1802.02627/) (2018)
- [Arbitrary Style Transfer with Style-Attentional Networks](https://mlsanity.com/paper/1812.02342/) (2018)
- [Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks](https://mlsanity.com/paper/1604.04382/) (2016)
- [Texture Networks: Feed-forward Synthesis of Textures and Stylized Images](https://mlsanity.com/paper/1603.03417/) (2016)
- [Deep Photo Style Transfer](https://mlsanity.com/paper/1703.07511/) (2017)
- [Style Transfer by Relaxed Optimal Transport and Self-Similarity](https://mlsanity.com/paper/1904.12785/) (2019)
- [Photorealistic Style Transfer via Wavelet Transforms](https://mlsanity.com/paper/1903.09760/) (2019)
- [Demystifying Neural Style Transfer](https://mlsanity.com/paper/1701.01036/) (2017)
- [A Closed-form Solution to Photorealistic Image Stylization](https://mlsanity.com/paper/1802.06474/) (2018)
- [Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis](https://mlsanity.com/paper/1601.04589/) (2016)
- [StyleBank: An Explicit Representation for Neural Image Style Transfer](https://mlsanity.com/paper/1703.09210/) (2017)
- [Dynamic Instance Normalization for Arbitrary Style Transfer](https://mlsanity.com/paper/1911.06953/) (2019)
- [Arbitrary Style Transfer with Deep Feature Reshuffle](https://mlsanity.com/paper/1805.04103/) (2018)
- [Stable and Controllable Neural Texture Synthesis and Style Transfer Using Histogram Losses](https://mlsanity.com/paper/1701.08893/) (2017)
- [Multi-style Generative Network for Real-time Transfer](https://mlsanity.com/paper/1703.06953/) (2017)
- [Diversified Texture Synthesis with Feed-forward Networks](https://mlsanity.com/paper/1703.01664/) (2017)
- [Collaborative Distillation for Ultra-Resolution Universal Style Transfer](https://mlsanity.com/paper/2003.08436/) (2020)
- [Stroke Controllable Fast Style Transfer with Adaptive Receptive Fields](https://mlsanity.com/paper/1802.07101/) (2018)
- [Multimodal Transfer: A Hierarchical Deep Convolutional Neural Network for Fast Artistic Style Transfer](https://mlsanity.com/paper/1612.01895/) (2016)
- [PhotoWCT2: Compact Autoencoder for Photorealistic Style Transfer Resulting from Blockwise Training and Skip Connections of High-Frequency Residuals](https://mlsanity.com/paper/2110.11995/) (2021)
- [Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks](https://mlsanity.com/paper/1603.01768/) (2016)
- [MicroAST: Towards Super-Fast Ultra-Resolution Arbitrary Style Transfer](https://mlsanity.com/paper/2211.15313/) (2022)
- [MetaStyle: Three-Way Trade-Off Among Speed, Flexibility, and Quality in Neural Style Transfer](https://mlsanity.com/paper/1812.05233/) (2018)
- [DivSwapper: Towards Diversified Patch-based Arbitrary Style Transfer](https://mlsanity.com/paper/2101.06381/) (2021)

## Abstract (arXiv)

The color and texture dual pipeline architecture (CTDP) suppresses texture representation and artifacts through masked total variation loss (Mtv), and further experiments have shown that smooth input can almost completely eliminate texture representation. We have demonstrated through experiments that smooth input is not the key reason for removing texture representations, but rather the distribution differentiation of the training dataset. Based on this, we propose an input distribution differentiation training strategy (IDD), which forces the generation of textures to be completely dependent on the noise distribution, while the smooth distribution will not produce textures at all. Overall, our proposed distribution differentiation training strategy allows for two pre-defined input distributions to be responsible for two generation tasks, with noise distribution responsible for texture generation and smooth distribution responsible for color smooth transfer. Finally, we choose a smooth distribution as the input for the forward inference stage to completely eliminate texture representations and artifacts in color transfer tasks.