https://github.com/uds-lsv/3st
공식 코드Exploiting Social Media Content for Self-Supervised Style Transfer
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Exploiting Social Media Content for Self-Supervised Style Transfer
Preventing Author Profiling through Zero-Shot Multilingual Back-Translation
UStyle: Waterbody Style Transfer of Underwater Scenes by Depth-Guided Feature Synthesis
Noise transfer for unsupervised domain adaptation of retinal OCT images
Color Prompting for Data-Free Continual Unsupervised Domain Adaptive Person Re-Identification
Disentangled Representation Learning for Non-Parallel Text Style Transfer
Improving Iterative Text Revision by Learning Where to Edit from Other Revision Tasks
Motion Style Transfer: Modular Low-Rank Adaptation for Deep Motion Forecasting
Font Completion and Manipulation by Cycling Between Multi-Modality Representations
SinGAN-Seg: Synthetic training data generation for medical image segmentation
Learning Evaluation Models from Large Language Models for Sequence Generation
Style-Specific Neurons for Steering LLMs in Text Style Transfer
Mitigating Negative Style Transfer in Hybrid Dialogue System
Pastiche Master: Exemplar-Based High-Resolution Portrait Style Transfer
VToonify: Controllable High-Resolution Portrait Video Style Transfer
A Plug-in Method for Representation Factorization in Connectionist Models
End-to-End Zero-Shot Voice Conversion with Location-Variable Convolutions
Creative Birds: Self-Supervised Single-View 3D Style Transfer
Creative Birds: Self-Supervised Single-View 3D Style Transfer
PSGAN++: Robust Detail-Preserving Makeup Transfer and Removal
Diverse Text Generation via Variational Encoder-Decoder Models with Gaussian Process Priors
AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer
Paint Transformer: Feed Forward Neural Painting with Stroke Prediction
Learning to Incorporate Texture Saliency Adaptive Attention to Image Cartoonization
Deliberated Domain Bridging for Domain Adaptive Semantic Segmentation
ConRF: Zero-shot Stylization of 3D Scenes with Conditioned Radiation Fields
Retriever: Learning Content-Style Representation as a Token-Level Bipartite Graph
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.