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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/guangmingzhu/sketchesc
공식 코드Enhance Sketch Recognition's Explainability via Semantic Component-Level Parsing
https://github.com/guangyliu/EISL
공식 코드Don't Take It Literally: An Edit-Invariant Sequence Loss for Text Generation
https://github.com/gudgud96/music-fader-nets
공식 코드https://github.com/guoyaoshen/rnst-regularization_by_nst
공식 코드Regularization by Neural Style Transfer for MRI Field-Transfer Reconstruction with Limited Data
https://github.com/h4nwei/deepdc
공식 코드DeepDC: Deep Distance Correlation as a Perceptual Image Quality Evaluator
https://github.com/ha-lins/DTG-SI
공식 코드https://github.com/haha-lisa/CreativeSynth
공식 코드CreativeSynth: Cross-Art-Attention for Artistic Image Synthesis with Multimodal Diffusion
https://github.com/haha-lisa/Diffstyler
공식 코드DiffStyler: Controllable Dual Diffusion for Text-Driven Image Stylization
https://github.com/haha-lisa/style-a-video
공식 코드Style-A-Video: Agile Diffusion for Arbitrary Text-based Video Style Transfer
https://github.com/halbertch/tssat
공식 코드TSSAT: Two-Stage Statistics-Aware Transformation for Artistic Style Transfer
https://github.com/hangwei-chen/ast-iqad-srqe
공식 코드Quality Evaluation of Arbitrary Style Transfer: Subjective Study and Objective Metric
https://github.com/hanjanghoon/Weighted_NS
공식 코드https://github.com/hanqi-qi/matte
공식 코드https://github.com/hansam95/osasis
공식 코드https://github.com/hanwen0529/DSCSI-GAN
공식 코드Stain Style Transfer of Histopathology Images Via Structure-Preserved Generative Learning
https://github.com/hanwu3125/os-sketch
공식 코드One-shot Face Sketch Synthesis in the Wild via Generative Diffusion Prior and Instruction Tuning
https://github.com/haoheliu/AudioLDM
공식 코드AudioLDM: Text-to-Audio Generation with Latent Diffusion Models
https://github.com/haoningwu3639/StoryGen
공식 코드Intelligent Grimm -- Open-ended Visual Storytelling via Latent Diffusion Models
https://github.com/happylittlecat2333/Auffusion
공식 코드Auffusion: Leveraging the Power of Diffusion and Large Language Models for Text-to-Audio Generation
https://github.com/haryoa/stif-indonesia
공식 코드https://github.com/hayeong0/DDDM-VC
공식 코드https://github.com/hayeong0/Diff-HierVC
공식 코드https://github.com/hila-chefer/targetclip
공식 코드https://github.com/hkust-vgd/architectural_style_transfer
공식 코드Time-of-Day Neural Style Transfer for Architectural Photographs
https://github.com/hnuzhy/ssda-yolo
공식 코드SSDA-YOLO: Semi-supervised Domain Adaptive YOLO for Cross-Domain Object Detection
https://github.com/hoenigpeter/randomized_texturing
공식 코드https://github.com/hologerry/Attr2Font
공식 코드https://github.com/hongyehu/RG-Flow
공식 코드RG-Flow: A hierarchical and explainable flow model based on renormalization group and sparse prior
https://github.com/horsepurve/StyleSegor
공식 코드Neural Style Transfer Improves 3D Cardiovascular MR Image Segmentation on Inconsistent Data
https://github.com/huage001/adaattn
공식 코드AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer
https://github.com/huage001/dynast
공식 코드DynaST: Dynamic Sparse Transformer for Exemplar-Guided Image Generation
https://github.com/huage001/painttransformer
공식 코드Paint Transformer: Feed Forward Neural Painting with Stroke Prediction
https://github.com/huage001/transfer-any-style
공식 코드Any-to-Any Style Transfer: Making Picasso and Da Vinci Collaborate
https://github.com/hubertsgithub/style_painting_robustness
공식 코드What Can Style Transfer and Paintings Do For Model Robustness?
https://github.com/hyeonseob-nam/Batch-Instance-Normalization
공식 코드Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.