https://github.com/textdetox/multilingual_explainable_paradetox
공식 코드Multilingual and Explainable Text Detoxification with Parallel Corpora
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Multilingual and Explainable Text Detoxification with Parallel Corpora
Pluggable Style Representation Learning for Multi-Style Transfer
Generalized One-shot Domain Adaptation of Generative Adversarial Networks
Portrait Stylization: Artistic Style Transfer with Auxiliary Networks for Human Face Stylization
NAST: A Non-Autoregressive Generator with Word Alignment for Unsupervised Text Style Transfer
Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer
Semi-supervised Human Pose Estimation in Art-historical Images
AnyV2V: A Tuning-Free Framework For Any Video-to-Video Editing Tasks
Fusion of complementary 2D and 3D mesostructural datasets using generative adversarial networks
Multiscale style transfer based on a Laplacian pyramid for traditional Chinese painting
StyleDyRF: Zero-shot 4D Style Transfer for Dynamic Neural Radiance Fields
Generating similes effortlessly like a Pro: A Style Transfer Approach for Simile Generation
DPStyler: Dynamic PromptStyler for Source-Free Domain Generalization
Improving Diffusion Models for Scene Text Editing with Dual Encoders
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
Enabling My Robot To Play Pictionary : Recurrent Neural Networks For Sketch Recognition
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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.