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AwesomeAnimeResearch

动漫研究前沿探索 从数据集到生成技术

AwesomeAnimeResearch汇集了动漫和漫画相关信号处理与机器学习研究的最新成果。项目涵盖数据集构建、图像生成和翻译等多个领域,提供动漫人物识别、风格迁移和少样本学习等热门主题的研究资源。这些内容有助于推动动漫AI技术的发展与创新。

AwesomeAnimeResearch

signal processing or machine learning related to anime or manga

Papers

Summary of published or preprint papers

Dataset

Image Generation

SubcategoryPaperConferenceLinks
GenerationTowards the Automatic Anime Characters Creation with Generative Adversarial NetworksComiket92HP
Full-body High-resolution Anime Generation with Progressive Structure-conditional Generative Adversarial NetworksECCVW2018HP
Generate Novel Image Styles using Weighted Hybrid Generative Adversarial NetsIJCNN2018
Towards Diverse Anime Face Generation: Active Label Completion and Style Feature NetworkEUROGRAPHICS2019
An Adaptive Control Algorithm for Stable Training of Generative Adversarial NetworksIEEE Access2019
Overcoming Long-term Catastrophic Forgetting through Adversarial Neural Pruning and Synaptic Consolidation
Autoencoding Generative Adversarial NetworksGithub
Classification Representations Can be Reused for Downstream Generations
GAN Memory with No ForgettingNeurIPS2020Github
Generating Full-Body Standing Figures of Anime Characters and Its Style Transfer by GAN
HistoGAN: Controlling Colors of GAN-Generated and Real Images via Color HistogramsCVPR2021Github
Efficient Continual Adaptation for Generative Adversarial Networks
Generating "Ideal" Anime Opening Frames Using Neural NetworksElConRus2021
CoPE: Conditional image generation using Polynomial Expansions
StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image GeneratorsSIGGRAPH2022Github
DisUnknown: Distilling Unknown Factors for Disentanglement LearningICCV2021Github
Combating Mode Collapse in GANs via Manifold Entropy EstimationGithub
Few-shotImage Generation From Small Datasets via Batch Statistics AdaptationICCV2019Github
FEW-SHOT ADAPTATION OF GENERATIVE ADVERSARIAL NETWORKSGithub
MineGAN: effective knowledge transfer from GANs to target domains with few imagesCVPR2020Github
Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANsCVPRW2020Github
DATA INSTANCE PRIOR FOR TRANSFER LEARNING IN GANS
MineGAN++: Mining Generative Models for Efficient Knowledge Transfer to Limited Data Domains
Data InStance Prior (DISP) in Generative Adversarial NetworksWACV2022
Controlling StyleGANs Using Rough Scribbles via One-shot LearningCAVW2022HP
InterpretabilityRPGAN: GANs Interpretability via Random RoutingGithub
Unsupervised Discovery of Interpretable Directions in the GAN Latent SpaceICML2020Github
Closed-Form Factorization of Latent Semantics in GANsCVPR2021Github
Unsupervised Discovery of Disentangled Manifolds in GANs
Do Generative Models Know Disentanglement? Contrastive Learning is All You NeedGithub
Surrogate Gradient Field for Latent Space ManipulationCVPR2021
EigenGAN: Layer-Wise Eigen-Learning for GANsGithub
Discovering Interpretable Latent Space Directions of GANs Beyond Binary AttributesCVPR2021
Discovering Density-Preserving Latent Space Walks in GANs for Semantic Image TransformationsMM 2021
Self-supervised Enhancement of Latent Discovery in GANsAAAI2022
Unsupervised Discovery of Disentangled Interpretable Directions for Layer-Wise GANBig Data2022
MontageMontageGAN: Generation and Assembly of Multiple Components by GANsICPR2022Github
Sprite-from-Sprite: Cartoon Animation Decomposition with Self-supervised Sprite EstimationTOG2022
Text-to-ImageAdding Conditional Control to Text-to-Image Diffusion ModelsGithub
DreamArtist: Towards Controllable One-Shot Text-to-Image Generation via Positive-Negative Prompt-TuningGithub

Image-to-image Translation

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