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Image Generation
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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/sqflow/sqflow
공식 코드Squeeze flow of micro-droplets: convolutional neural network with trainable and tunable refinement
https://github.com/sreyan88/aspire
공식 코드ASPIRE: Language-Guided Data Augmentation for Improving Robustness Against Spurious Correlations
https://github.com/srijay-lab/synclay-framework
공식 코드SynCLay: Interactive Synthesis of Histology Images from Bespoke Cellular Layouts
https://github.com/ssalazarcolores/Desmoking-laparoscopy-surgery-images-using-an-image-to-image-translation-guided-by-an-embedded-Dark-
공식 코드https://github.com/sssohrab/rho_VAE
공식 코드https://github.com/sstzal/DFRF
공식 코드Learning Dynamic Facial Radiance Fields for Few-Shot Talking Head Synthesis
https://github.com/sstzal/DiffTalk
공식 코드DiffTalk: Crafting Diffusion Models for Generalized Audio-Driven Portraits Animation
https://github.com/ssundaram21/personalized-rep
공식 코드https://github.com/sswuai/panogan
공식 코드https://github.com/stability-ai/generative-models
공식 코드Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models
https://github.com/stanifrolov/attrlostgan
공식 코드AttrLostGAN: Attribute Controlled Image Synthesis from Reconfigurable Layout and Style
https://github.com/stanifrolov/spotdiffusion
공식 코드SpotDiffusion: A Fast Approach For Seamless Panorama Generation Over Time
https://github.com/stefanleong/s2crnet
공식 코드Spatial-Separated Curve Rendering Network for Efficient and High-Resolution Image Harmonization
https://github.com/steffen-jung/SpectralGAN
공식 코드https://github.com/steffen-jung/SpectralGAN-StyleGAN2
공식 코드https://github.com/stepfun-ai/step1x-edit
공식 코드Step1X-Edit: A Practical Framework for General Image Editing
https://github.com/stephenivy07/Landmarkgan
공식 코드https://github.com/sterzhang/image-textualization
공식 코드Image Textualization: An Automatic Framework for Creating Accurate and Detailed Image Descriptions
https://github.com/sterzhang/pvit
공식 코드https://github.com/stevengrove/gpt4tools
공식 코드GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction
https://github.com/stevliu/self-conditioned-gan
공식 코드https://github.com/studying910/BaTex
공식 코드Efficient Personalized Text-to-image Generation by Leveraging Textual Subspace
https://github.com/stylegan-human/stylegan-human
공식 코드https://github.com/submissionanon18/agg
공식 코드Improving Diffusion-based Image Translation using Asymmetric Gradient Guidance
https://github.com/suetAndTie/cycle-image-gan
공식 코드https://github.com/sugarfreemanatee/feature-space-augmentation-and-iterated-learning
공식 코드Long Tail Image Generation Through Feature Space Augmentation and Iterated Learning
https://github.com/suhas-srinath/genselfdiff-his
공식 코드https://github.com/sukarnabarua/fccgan
공식 코드FCC-GAN: A Fully Connected and Convolutional Net Architecture for GANs
https://github.com/sulam-group/k-rcps
공식 코드How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control
https://github.com/sungnyun/diffblender
공식 코드DiffBlender: Scalable and Composable Multimodal Text-to-Image Diffusion Models
https://github.com/sunnierlee/dp-feta
공식 코드From Easy to Hard: Building a Shortcut for Differentially Private Image Synthesis
https://github.com/sunnierlee/dp-imagen
공식 코드https://github.com/sunnyhaze/iml-vit
공식 코드IML-ViT: Benchmarking Image Manipulation Localization by Vision Transformer
https://github.com/sunzey/alphaclip
공식 코드https://github.com/sunzey/x-prompt
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.