- Texture Synthesis Using Convolutional Neural Networks
- A Neural Algorithm of Artistic Style
- Texture Networks: Feed-forward Synthesis of Textures and Stylized Images
- Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Texture Synthesis
- Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis
- Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks
- Visual Attribute Transfer through Deep Image Analogy
- Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
- Dynamic Instance Normalization for Arbitrary Style Transfer
- Style and Content Disentanglement in Generative Adversarial Networks
- Non-Stationary Texture Synthesis by Adversarial Expansion
- Texture Mixing by Interpolating Deep Statistics via Gaussian Models
- Fast Patch-based Style Transfer of Arbitrary Style
- Universal Style Transfer via Feature Transforms
- Multimodal Transfer: A Hierarchical Deep Convolutional Neural Network for Fast Artistic Style Transfer
- NITES: A Non-Parametric Interpretable Texture Synthesis Method
- Inverse Texture Synthesis
- Transposer: Universal Texture Synthesis Using Feature Maps as Transposed Convolution Filter
- Neural FFTs for Universal Texture Image Synthesis
- Texture Synthesis Using Shallow Convolutional Networks with Random Filters
- Texture Synthesis with Spatial Generative Adversarial Networks
- Learning Texture Manifolds with the Periodic Spatial GAN
- On Demand Solid Texture Synthesis Using Deep 3D Networks
- A Style-Aware Content Loss for Real-time HD Style Transfer
- Optimal Textures: Fast and Robust Texture Synthesis and Style Transfer through Optimal Transport
- A Survey of Exemplar-based Texture Synthesis
- Neural Style Transfer: A Review
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