Papers with Code 아카이브

Image Generation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 23
평가표: 93
보존된 평가 기록: 870

Image Generation (synthesis) is the task of generating new images from an existing dataset.

Unconditional generation refers to generating samples unconditionally from the dataset, i.e. $p(y)$
Conditional image generation (subtask) refers to generating samples conditionally from the dataset, based on a label, i.e. $p(y|x)$.

In this section, you can find state-of-the-art leaderboards for unconditional generation. For conditional generation, and other types of image generations, refer to the subtasks.

<span style="color:grey; opacity: 0.6">( Image credit: StyleGAN )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/ajwad-abrar/religious-bias

공식 코드

Religious Bias Landscape in Language and Text-to-Image Models: Analysis, Detection, and Debiasing Strategies

https://github.com/akanimax/msg-stylegan-tf

공식 코드

MSG-GAN: Multi-Scale Gradients for Generative Adversarial Networks

https://github.com/akashpalrecha/hyperspectral-reconstruction

공식 코드

MXR-U-Nets for Real Time Hyperspectral Reconstruction

https://github.com/akirahero/diffindscene

공식 코드

DiffInDScene: Diffusion-based High-Quality 3D Indoor Scene Generation

https://github.com/akmtn/OmniDreamer

공식 코드

Diverse Plausible 360-Degree Image Outpainting for Efficient 3DCG Background Creation

https://github.com/akomand/causaldiffae

공식 코드

Causal Diffusion Autoencoders: Toward Counterfactual Generation via Diffusion Probabilistic Models

https://github.com/akuzina/dvp_vae

공식 코드

Hierarchical VAE with a Diffusion-based VampPrior

https://github.com/albarji/mixture-of-diffusers

공식 코드

Mixture of Diffusers for scene composition and high resolution image generation

https://github.com/albertpumarola/GANimation

공식 코드

GANimation: Anatomically-aware Facial Animation from a Single Image

https://github.com/aleeehu/world-simulator

공식 코드

Simulating the Real World: A Unified Survey of Multimodal Generative Models

https://github.com/alenjandrowang/asvr

공식 코드

Autoregressive Semantic Visual Reconstruction Helps VLMs Understand Better

https://github.com/alexanderswerdlow/BEVGen

공식 코드

Street-View Image Generation from a Bird's-Eye View Layout

https://github.com/alexanderswerdlow/unidisc

공식 코드

Unified Multimodal Discrete Diffusion

https://github.com/alexandonian/contrastive-feature-loss

공식 코드

Contrastive Feature Loss for Image Prediction

https://github.com/alexbie98/dpgan-revisit

공식 코드

Private GANs, Revisited

https://github.com/alexmaols/elucd

공식 코드

Elucidating The Design Space of Classifier-Guided Diffusion Generation

https://github.com/alexmartin1722/Revive-2I

공식 코드

Jurassic World Remake: Bringing Ancient Fossils Back to Life via Zero-Shot Long Image-to-Image Translation

https://github.com/alexrame/rewardedsoups

공식 코드

Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards

https://github.com/alexverine/pr-4-llm

공식 코드

Exploring Precision and Recall to assess the quality and diversity of LLMs

https://github.com/alexzhou907/DDBM

공식 코드

Denoising Diffusion Bridge Models

https://github.com/alexzhou907/dreampropeller

공식 코드

DreamPropeller: Supercharge Text-to-3D Generation with Parallel Sampling

https://github.com/algolzw/daclip-uir

공식 코드

Controlling Vision-Language Models for Multi-Task Image Restoration

https://github.com/algolzw/image-restoration-sde

공식 코드

Image Restoration with Mean-Reverting Stochastic Differential Equations

https://github.com/algue-rythme/oneclassmetriclearning

공식 코드

Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networks

https://github.com/ali-vilab/FACM

공식 코드

Flow-Anchored Consistency Models

https://github.com/ali-vilab/In-Context-LoRA

공식 코드

In-Context LoRA for Diffusion Transformers

https://github.com/ali-vilab/VGen

공식 코드

DreamVideo: Composing Your Dream Videos with Customized Subject and Motion

https://github.com/ali-vilab/alitok

공식 코드

AliTok: Towards Sequence Modeling Alignment between Tokenizer and Autoregressive Model

https://github.com/ali-vilab/dreamtalk

공식 코드

DreamTalk: When Emotional Talking Head Generation Meets Diffusion Probabilistic Models

https://github.com/alibaba-damo-academy/DyDiT

공식 코드

DyDiT++: Dynamic Diffusion Transformers for Efficient Visual Generation

https://github.com/alibaba-yuanjing-aigclab/geolrm

공식 코드

GeoLRM: Geometry-Aware Large Reconstruction Model for High-Quality 3D Gaussian Generation

https://github.com/alibaba/EasyNLP/tree/master/diffusion/olss_scheduler

공식 코드

Optimal Linear Subspace Search: Learning to Construct Fast and High-Quality Schedulers for Diffusion Models

https://github.com/aliborji/gfw

공식 코드

Generated Faces in the Wild: Quantitative Comparison of Stable Diffusion, Midjourney and DALL-E 2

https://github.com/alicex2020/Chinese-Landscape-Painting-Dataset

공식 코드

End-to-End Chinese Landscape Painting Creation Using Generative Adversarial Networks

https://github.com/alinlab/b2t

공식 코드

Discovering and Mitigating Visual Biases through Keyword Explanation

https://github.com/alipay/style-tokenizer

공식 코드

StyleTokenizer: Defining Image Style by a Single Instance for Controlling Diffusion Models

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.