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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Z-T-WANG/LaProp-Optimizer
공식 코드https://github.com/ZHKKKe/NeuralPreset
공식 코드https://github.com/ZJULearning/CariFaceParsing
공식 코드Weakly-supervised Caricature Face Parsing through Domain Adaptation
https://github.com/ZZUTK/SRNTT
공식 코드https://github.com/ZZWaang/polyphonic-chord-texture-disentanglement
공식 코드Learning Interpretable Representation for Controllable Polyphonic Music Generation
https://github.com/ZhongliangGuo/LAACA
공식 코드Artwork Protection Against Neural Style Transfer Using Locally Adaptive Adversarial Color Attack
https://github.com/ZszYmy9/Puff-Net
공식 코드Puff-Net: Efficient Style Transfer with Pure Content and Style Feature Fusion Network
https://github.com/aaronz345/gtsinger
공식 코드GTSinger: A Global Multi-Technique Singing Corpus with Realistic Music Scores for All Singing Tasks
https://github.com/aaronz345/tcsinger2
공식 코드TCSinger 2: Customizable Multilingual Zero-shot Singing Voice Synthesis
https://github.com/abertsch72/perspective-shifting
공식 코드He Said, She Said: Style Transfer for Shifting the Perspective of Dialogues
https://github.com/abhibha1807/robustifying_visual_perception
공식 코드https://github.com/abhinavkashyap/dct
공식 코드So Different Yet So Alike! Constrained Unsupervised Text Style Transfer
https://github.com/adobe-research/domain-expansion
공식 코드HyperGAN-CLIP: A Unified Framework for Domain Adaptation, Image Synthesis and Manipulation
https://github.com/afrozas/skip-thought-gan
공식 코드Generating Text through Adversarial Training using Skip-Thought Vectors
https://github.com/agwabom/pema
공식 코드PEMA: An Offsite-Tunable Plug-in External Memory Adaptation for Language Models
https://github.com/aigagror/wasserstein-style-transfer
공식 코드https://github.com/aioz-ai/audiodrivenstyletransfer
공식 코드https://github.com/air-discover/lasst
공식 코드https://github.com/airl-iisc/MRFP
공식 코드https://github.com/akashpalrecha/hyperspectral-reconstruction
공식 코드https://github.com/akshaymehra24/riskaversedg
공식 코드On the Fly Neural Style Smoothing for Risk-Averse Domain Generalization
https://github.com/alexrodpas/BACON
공식 코드BACON: Deep-Learning Powered AI for Poetry Generation with Author Linguistic Style Transfer
https://github.com/aliaksandrsiarohin/bae
공식 코드Enhancing Perceptual Attributes with Bayesian Style Generation
https://github.com/aliayub7/EEC
공식 코드EEC: Learning to Encode and Regenerate Images for Continual Learning
https://github.com/alienzhang1996/s2wat
공식 코드S2WAT: Image Style Transfer via Hierarchical Vision Transformer using Strips Window Attention
https://github.com/allambert/torch_itl
공식 코드https://github.com/allenai/tailor
공식 코드Tailor: Generating and Perturbing Text with Semantic Controls
https://github.com/amirdahari1/superres
공식 코드Fusion of complementary 2D and 3D mesostructural datasets using generative adversarial networks
https://github.com/angelvillar96/STLPose
공식 코드https://github.com/anon-nlp-1234/da-nlg
공식 코드https://github.com/anon294384/diffuseit
공식 코드Diffusion-based Image Translation using Disentangled Style and Content Representation
https://github.com/aramir62/da-nlg
공식 코드https://github.com/architect-road/icongan
공식 코드Design What You Desire: Icon Generation from Orthogonal Application and Theme Labels
https://github.com/arnab39/Semi-supervised-segmentation-cycleGAN
공식 코드https://github.com/artiprocher/sd-webui-fastblend
공식 코드FastBlend: a Powerful Model-Free Toolkit Making Video Stylization Easier
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.