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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/sofeeyaj/poetry-in-pixels-coling2025
공식 코드Poetry in Pixels: Prompt Tuning for Poem Image Generation via Diffusion Models
https://github.com/softrepa/SoftREPA
공식 코드Aligning Text to Image in Diffusion Models is Easier Than You Think
https://github.com/soham97/pam
공식 코드PAM: Prompting Audio-Language Models for Audio Quality Assessment
https://github.com/sohyun-l/ExLPose
공식 코드https://github.com/songrise/Artist
공식 코드DiffArtist: Towards Structure and Appearance Controllable Image Stylization
https://github.com/songrise/mllm4art
공식 코드https://github.com/songweige/rich-text-to-image
공식 코드https://github.com/sonnguyen129/deep-feature-rotation
공식 코드https://github.com/sony/gibbsddrm
공식 코드https://github.com/sony/model_optimization
공식 코드https://github.com/sonyresearch/coala
공식 코드COALA: A Practical and Vision-Centric Federated Learning Platform
https://github.com/soon-yau/upgpt
공식 코드UPGPT: Universal Diffusion Model for Person Image Generation, Editing and Pose Transfer
https://github.com/soon-yau/visconet
공식 코드ViscoNet: Bridging and Harmonizing Visual and Textual Conditioning for ControlNet
https://github.com/sooyyoungg/aesfa
공식 코드AesFA: An Aesthetic Feature-Aware Arbitrary Neural Style Transfer
https://github.com/sophiajw/HistAuGAN
공식 코드https://github.com/soullessrobot/human-m3-dataset
공식 코드Human-M3: A Multi-view Multi-modal Dataset for 3D Human Pose Estimation in Outdoor Scenes
https://github.com/soullessrobot/lidar-hmr
공식 코드https://github.com/soumickmj/TorchEsegeta
공식 코드Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images
https://github.com/soumickmj/diagnoPP
공식 코드Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images
https://github.com/souro/multilingual_text_detoxification
공식 코드https://github.com/souro/multilingual_tst
공식 코드Multilingual Text Style Transfer: Datasets & Models for Indian Languages
https://github.com/souro/polarity-denoising-sentiment-transfer
공식 코드Balancing the Style-Content Trade-Off in Sentiment Transfer Using Polarity-Aware Denoising
https://github.com/souro/tst_llm
공식 코드Are Large Language Models Actually Good at Text Style Transfer?
https://github.com/sowwnn/KFusion-Dual-Domain-for-Speech-to-Landmarks
공식 코드KAN-Based Fusion of Dual-Domain for Audio-Driven Facial Landmarks Generation
https://github.com/spaceuma/marssamplelocalization
공식 코드https://github.com/spla-tam/splatam
공식 코드SplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAM
https://github.com/sr42-dev/pedestrian-intention-fuzzy-classifier
공식 코드https://github.com/sreyan88/aspire
공식 코드ASPIRE: Language-Guided Data Augmentation for Improving Robustness Against Spurious Correlations
https://github.com/ssfootball04/generative_pose
공식 코드Monocular 3D Human Pose Estimation by Generation and Ordinal Ranking
https://github.com/sshan-zhao/GASDA
공식 코드Geometry-Aware Symmetric Domain Adaptation for Monocular Depth Estimation
https://github.com/sstzal/DFRF
공식 코드Learning Dynamic Facial Radiance Fields for Few-Shot Talking Head Synthesis
https://github.com/stability-ai/generative-models
공식 코드Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models
https://github.com/stability-ai/stable-audio-tools
공식 코드https://github.com/stanfordasl/data-lifecycle-management
공식 코드Data Lifecycle Management in Evolving Input Distributions for Learning-based Aerospace Applications
https://github.com/stefan-ainetter/SCANnotateDataset
공식 코드Automatically Annotating Indoor Images with CAD Models via RGB-D Scans
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.