Papers with Code 아카이브
Image-to-Image Translation
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- 하위 태스크
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- 평가표
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- 보존된 평가 기록
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Image-to-Image Translation is a task in computer vision and machine learning where the goal is to learn a mapping between an input image and an output image, such that the output image can be used to perform a specific task, such as style transfer, data augmentation, or image restoration.
<span style="color:grey; opacity: 0.6">( Image credit: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Single-image Reflectance and Transmittance Estimation from Any Flatbed Scanner
Semantically Robust Unsupervised Image Translation for Paired Remote Sensing Images
Unpaired Image-to-Image Translation with Content Preserving Perspective: A Review
Overcoming Fake Solutions in Semi-Dual Neural Optimal Transport: A Smoothing Approach for Learning the Optimal Transport Plan
Transferring Graph Neural Networks for Soft Sensor Modeling using Process Topologies
Learning to Synthesize Compatible Fashion Items Using Semantic Alignment and Collocation Classification: An Outfit Generation Framework
Style transfer as data augmentation: evaluating unpaired image-to-image translation models in mammography
FCBoost-Net: A Generative Network for Synthesizing Multiple Collocated Outfits via Fashion Compatibility Boosting
Influence of High-Performance Image-to-Image Translation Networks on Clinical Visual Assessment and Outcome Prediction: Utilizing Ultrasound to MRI Translation in Prostate Cancer
Trustworthy image-to-image translation: evaluating uncertainty calibration in unpaired training scenarios
Generative AI for Vision: A Comprehensive Study of Frameworks and Applications
EfficientVITON: An Efficient Virtual Try-On Model using Optimized Diffusion Process
Knowledge Distillation for Image Restoration : Simultaneous Learning from Degraded and Clean Images
A Soft Sensor Method with Uncertainty-Awareness and Self-Explanation Based on Large Language Models Enhanced by Domain Knowledge Retrieval
NijiGAN: Transform What You See into Anime with Contrastive Semi-Supervised Learning and Neural Ordinary Differential Equations
Diffusion-Based Conditional Image Editing through Optimized Inference with Guidance
LiHi-GS: LiDAR-Supervised Gaussian Splatting for Highway Driving Scene Reconstruction
IGR: Improving Diffusion Model for Garment Restoration from Person Image
Go With the Flow: Fast Diffusion for Gaussian Mixture Models
TryOffAnyone: Tiled Cloth Generation from a Dressed Person
DAug: Diffusion-based Channel Augmentation for Radiology Image Retrieval and Classification
MRNet: Multifaceted Resilient Networks for Medical Image-to-Image Translation
Distillation of Diffusion Features for Semantic Correspondence
SimuScope: Realistic Endoscopic Synthetic Dataset Generation through Surgical Simulation and Diffusion Models
Fréchet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imaging Datasets
TryOffDiff: Virtual-Try-Off via High-Fidelity Garment Reconstruction using Diffusion Models
cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image Synthesis
Uncertainty-Aware Regularization for Image-to-Image Translation
Capacitive Touch Sensor Modeling With a Physics-informed Neural Network and Maxwell's Equations
Learning Modality-Aware Representations: Adaptive Group-wise Interaction Network for Multimodal MRI Synthesis
Latent Schrodinger Bridge: Prompting Latent Diffusion for Fast Unpaired Image-to-Image Translation
C-DiffSET: Leveraging Latent Diffusion for SAR-to-EO Image Translation with Confidence-Guided Reliable Object Generation
Mechanisms of Generative Image-to-Image Translation Networks
SAM-I2I: Unleash the Power of Segment Anything Model for Medical Image Translation
Scaling Properties of Diffusion Models for Perceptual Tasks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.