Image Style Transfer Using Convolutional Neural Networks

By Leon A. Gatys, Alexander S. Ecker, Matthias Bethge. 2016

5,793 citations recorded by the source corpus.

Subfields: Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition, Generative Models

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Graphics and Computer-Aided Design

In Computer Vision and Pattern Recognition

In Generative Models

Citations in the corpus

Fixing the train-test resolution discrepancy (2019, background): “Convolutional Neural Networks [21] (CNNs) are used extensively in computer vision tasks such as image classification [20], object detection [30], inpainting [41], style transfer [11] and even image compression [31].”
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations (2019, neutral): “When perturbations have a high propensity to change the model’s response, then perturbations could also misdirect or destabilize iterative image optimization procedures appearing in style transfer (Gatys et al., 2016), decision explanations (Fong & Vedaldi, 2017), feature visualization (Olah et al.”
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness (2018, methodology): “They were generated using iterative style transfer (Gatys et al., 2016) between a texture image (from the texture experiment described above) and a content image (from the original experiment) each.”
A Style-Based Generator Architecture for Generative Adversarial Networks (2018, background): “) reliably encode the style of an image [19, 36] while spatially varying features encode a specific instance.”
Generative Adversarial Networks (2021, neutral): “In addition to image synthesis, GANs have been applied to a wide range of problems, s.a. data augmentation, style transfer [9], and anomaly detection [10].”
On Buggy Resizing Libraries and Surprising Subtleties in FID Calculation (2021, background): “Deep network activations are used as the statistics of interest, as they have been shown to correspond well with human perceptual judgments [80] and are often used as training objectives [20, 26, 33].”
Object-Centric Image Generation from Layouts (2020, neutral): “Perceptual loss We found that adding a perceptual loss [7,10,22] to our model improved results slightly.”
EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning (2019, methodology): “We include the two losses proposed in [16, 24] commonly known as perceptual loss Lperc and style loss Lstyle.”
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric (2018, methodology): “For example, features from the VGG architecture [51] have been used on tasks such as neural style transfer [17], image superresolution [23], and conditional image synthesis [14, 8].”
A critical analysis of self-supervision, or what we can learn from a single image (2019, methodology): “In Figure 3 we show two basic style transfers using the method of (Gatys et al., 2016) from an ofﬁcial PyTorch tutorial 3 .”
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs (2017, methodology): “Deep visual manipulation Recently, deep neural networks have obtained promising results in various image processing tasks, such as style transfer [13], inpainting [40], colorization [56], and restoration [14].”
Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks (2017, methodology): “Neural Style Transfer [12, 22, 51, 11] is another way to perform image-to-image translation, which synthesizes a novel image by combining the content of one image with the style of another image (typically a painting) based on matching the Gram matrix statistics of pre-trained deep features.”
Unsupervised Image-to-Image Translation Networks (2017, background): “We note that image translation is different to style transfer (Gatys et al., 2016; Johnson et al., 2016) in that while style transfer focuses on translate a natural image to an abstract, artistic image, image translation also considers translating a natural image to another natural image where loss ”
Learning from Simulated and Unsupervised Images through Adversarial Training (2016, neutral): “Style transfer [7] is also closely related to our work.”