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Piggyback GAN
The dataset used in the paper is not explicitly described, but it is mentioned that the authors used it for lifelong learning of generative networks. -
Expertise Style Transfer
Expertise Style Transfer: A New Task Towards Better Communication between Experts and Laymen. -
Improved Object-Based Style Transfer with Single Deep Network
The proposed approach uses a single deep network of YOLOv8 for both segmentation and style transfer. -
Style-guided generation with Stable Diffusion
The dataset used in this paper is a style-guided generation task with Stable Diffusion. The dataset contains images with different styles and prompts. -
E-WNC: Explainable Subjective Bias Style Transfer
We build two explainable style transfer datasets by augmenting existing datasets with synthetic textual explanations generated by a teacher model. -
E-GYAFC: Explainable Formality Style Transfer
We build two explainable style transfer datasets by augmenting existing datasets with synthetic textual explanations generated by a teacher model. -
ICLEF: In-Context Learning with Expert Feedback for Explainable Style
We build two explainable style transfer datasets by augmenting existing datasets with synthetic textual explanations generated by a teacher model. -
PACS dataset
The dataset used in the paper is a large collection of small images, each representing a patch of a jigsaw puzzle. The patches are of the same size and orientation, and the goal... -
Antholzer et al. dataset
A custom dataset created for this paper, consisting of 2107 point clouds, each with 16384 points, and three design point clouds: stripes, porous, and cut. -
Arbitrary Style Transfer
Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization. -
Stable Diffusion Prompts
The dataset used in the paper for text-to-image generation and style transfer tasks. -
Style Aligned Image Generation
StyleAligned is a method for generating style-consistent images using a reference style image. -
Style Conditioned Recommendations
The dataset used in this paper is a user-item click matrix with item content data and item style labels. -
A Style-Based Generator Architecture for Generative Adversarial Networks
A style-based generator architecture for generative adversarial networks. -
Denoising Diffusion Probabilistic Models for Styled Walking Synthesis
Motions are from two publicly available datasets: Xia et al. [Xia et al. 2015] and HumanAct12 [Guo et al. 2020].
