https://github.com/NVIDIA/flowtron
공식 코드Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis
Papers with Code 아카이브
Sketch
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 4
- 평가표
- 0
- 보존된 평가 기록
- 0
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/NevSNev/UniST
공식 코드Two Birds, One Stone: A Unified Framework for Joint Learning of Image and Video Style Transfers
https://github.com/Nithin-GK/UniteandConquer
공식 코드Unite and Conquer: Plug & Play Multi-Modal Synthesis using Diffusion Models
https://github.com/OmarSZamzam/PixelShuffler
공식 코드PixelShuffler: A Simple Image Translation Through Pixel Rearrangement
https://github.com/OndrejTexler/Few-Shot-Patch-Based-Training
공식 코드Interactive Video Stylization Using Few-Shot Patch-Based Training
https://github.com/PaddlePaddle/PaddleGAN
공식 코드Drafting and Revision: Laplacian Pyramid Network for Fast High-Quality Artistic Style Transfer
https://github.com/PaddlePaddle/PaddleHub
공식 코드Parameter-Free Style Projection for Arbitrary Style Transfer
https://github.com/PaddlePaddle/Research
공식 코드Exploring Contextual Word-level Style Relevance for Unsupervised Style Transfer
https://github.com/PeizhuoLi/ganimator
공식 코드https://github.com/PengBoXiangShang/multigraph_transformer
공식 코드https://github.com/QuanjianSong/UniVST
공식 코드UniVST: A Unified Framework for Training-free Localized Video Style Transfer
https://github.com/RUCAIBox/TextBox
공식 코드TextBox 2.0: A Text Generation Library with Pre-trained Language Models
https://github.com/Rainbow0204/HyperEditor
공식 코드https://github.com/Rongjiehuang/GenerSpeech
공식 코드GenerSpeech: Towards Style Transfer for Generalizable Out-Of-Domain Text-to-Speech
https://github.com/RoyalVane/ASM
공식 코드Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation
https://github.com/SHI-Labs/Rethinking-Text-Segmentation
공식 코드Rethinking Text Segmentation: A Novel Dataset and A Text-Specific Refinement Approach
https://github.com/ShangxuanWu/CycleGAN-Face-off
공식 코드https://github.com/Singularity42/cap2vid
공식 코드https://github.com/SonyResearch/diffvox
공식 코드Improving Inference-Time Optimisation for Vocal Effects Style Transfer with a Gaussian Prior
https://github.com/Spenhouet/automated-deep-photo-style-transfer
공식 코드https://github.com/TAMU-VITA/GAN-Slimming
공식 코드GAN Slimming: All-in-One GAN Compression by A Unified Optimization Framework
https://github.com/TAMU-VITA/ShapeMatchingGAN
공식 코드Controllable Artistic Text Style Transfer via Shape-Matching GAN
https://github.com/THUYimingLi/MOVE
공식 코드MOVE: Effective and Harmless Ownership Verification via Embedded External Features
https://github.com/ToyotaCRDL/Visual-Concept-Blending
공식 코드https://github.com/VITA-Group/Sandwich-Batch-Normalization
공식 코드Sandwich Batch Normalization: A Drop-In Replacement for Feature Distribution Heterogeneity
https://github.com/Varal7/blank_language_model
공식 코드https://github.com/VinAIResearch/CPM
공식 코드Lipstick ain't enough: Beyond Color Matching for In-the-Wild Makeup Transfer
https://github.com/WeiWangTrento/Robust-Differentiable-SVD
공식 코드https://github.com/WonwoongCho/GDWCT
공식 코드Image-to-Image Translation via Group-wise Deep Whitening-and-Coloring Transformation
https://github.com/XingangPan/Switchable-Whitening
공식 코드https://github.com/XuezheMax/wolf
공식 코드Decoupling Global and Local Representations via Invertible Generative Flows
https://github.com/YaoZhang93/Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer
공식 코드Semi-supervised Cardiac Image Segmentation via Label Propagation and Style Transfer
https://github.com/YatingMusic/MuseMorphose
공식 코드MuseMorphose: Full-Song and Fine-Grained Piano Music Style Transfer with One Transformer VAE
https://github.com/Yijunmaverick/UniversalStyleTransfer
공식 코드https://github.com/YooJiHyeong/SinIR
공식 코드SinIR: Efficient General Image Manipulation with Single Image Reconstruction
https://github.com/YouzhiTian/Structured-Content-Preservation-for-Unsupervised-Text-Style-Transfer
공식 코드Structured Content Preservation for Unsupervised Text Style Transfer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.