https://github.com/hangwei-chen/ast-iqad-srqe
공식 코드Quality Evaluation of Arbitrary Style Transfer: Subjective Study and Objective Metric
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
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/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
https://github.com/i2-multimedia-lab/diffusest
공식 코드DiffuseST: Unleashing the Capability of the Diffusion Model for Style Transfer
https://github.com/iamalexkorotin/Wasserstein2GenerativeNetworks
공식 코드https://github.com/ianxmason/local-phases
공식 코드https://github.com/iglict/stylizednerf
공식 코드StylizedNeRF: Consistent 3D Scene Stylization as Stylized NeRF via 2D-3D Mutual Learning
https://github.com/iiscleap/zest
공식 코드Zero Shot Audio to Audio Emotion Transfer With Speaker Disentanglement
https://github.com/iit-pavis/learnaug-uda
공식 코드Learnable Data Augmentation for One-Shot Unsupervised Domain Adaptation
https://github.com/ilyagusev/rudetox
공식 코드https://github.com/instantx-research/instantstyle-plus
공식 코드InstantStyle-Plus: Style Transfer with Content-Preserving in Text-to-Image Generation
https://github.com/isi-nlp/astrapop
공식 코드https://github.com/isi-nlp/stamp
공식 코드https://github.com/isrugeek/semcolour
공식 코드Nanoscale Microscopy Images Colorization Using Neural Networks
https://github.com/itsss/img2img_sa
공식 코드Unsupervised Image-to-Image Translation with Self-Attention Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.