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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/mingxiao-li/towards-more-accurate-personalized-image-generation
공식 코드Towards More Accurate Personalized Image Generation: Addressing Overfitting and Evaluation Bias
https://github.com/mingyuanzhou/sid
공식 코드https://github.com/mingyuanzhou/sid-lsg
공식 코드Long and Short Guidance in Score identity Distillation for One-Step Text-to-Image Generation
https://github.com/mingyuliutw/CoGAN
공식 코드https://github.com/mingyuliutw/UNIT
공식 코드https://github.com/minhuiyu0418/FICD
공식 코드Functional Imaging Constrained Diffusion for Brain PET Synthesis from Structural MRI
https://github.com/minivision-ai/photo2cartoon
공식 코드Few-shot Knowledge Transfer for Fine-grained Cartoon Face Generation
https://github.com/minjeekim00/Barcode
공식 코드Barcode Method for Generative Model Evaluation driven by Topological Data Analysis
https://github.com/minqili/viton-drr-main
공식 코드VITON-DRR: Details Retention Virtual Try-on via Non-rigid Registration
https://github.com/minshu-kim/rewarp
공식 코드Learning Residual Elastic Warps for Image Stitching under Dirichlet Boundary Condition
https://github.com/mio7690/VLEU
공식 코드VLEU: a Method for Automatic Evaluation for Generalizability of Text-to-Image Models
https://github.com/miquel-espinosa/map-sat
공식 코드Generate Your Own Scotland: Satellite Image Generation Conditioned on Maps
https://github.com/mireillefares/vaanimation
공식 코드Transformer Network for Semantically-Aware and Speech-Driven Upper-Face Generation
https://github.com/mirthai/fast-ddpm
공식 코드Fast-DDPM: Fast Denoising Diffusion Probabilistic Models for Medical Image-to-Image Generation
https://github.com/mirthai/gdm-ve
공식 코드Geodesic Diffusion Models for Medical Image-to-Image Generation
https://github.com/misaka12435/tolo
공식 코드ToLo: A Two-Stage, Training-Free Layout-To-Image Generation Framework For High-Overlap Layouts
https://github.com/misaliet/seamless-satellite-image-synthesis
공식 코드https://github.com/mischad/beyondfid
공식 코드https://github.com/mischad/chest-distillation
공식 코드Trade-offs in Fine-tuned Diffusion Models Between Accuracy and Interpretability
https://github.com/mit-acl/dc2g
공식 코드https://github.com/mit-han-lab/anycost-gan
공식 코드https://github.com/mit-han-lab/data-efficient-gans
공식 코드https://github.com/mit-han-lab/efficientvit
공식 코드Deep Compression Autoencoder for Efficient High-Resolution Diffusion Models
https://github.com/mit-han-lab/fastcomposer
공식 코드FastComposer: Tuning-Free Multi-Subject Image Generation with Localized Attention
https://github.com/mit-han-lab/gan-compression
공식 코드GAN Compression: Efficient Architectures for Interactive Conditional GANs
https://github.com/mit-han-lab/hart
공식 코드HART: Efficient Visual Generation with Hybrid Autoregressive Transformer
https://github.com/mit-han-lab/vila-u
공식 코드VILA-U: a Unified Foundation Model Integrating Visual Understanding and Generation
https://github.com/mit-ll/bladerunner
공식 코드BLADERUNNER: Rapid Countermeasure for Synthetic (AI-Generated) StyleGAN Faces
https://github.com/mitscha/dplc
공식 코드Deep Generative Models for Distribution-Preserving Lossy Compression
https://github.com/miu200521358/NeuralVoicePuppetryMMD
공식 코드https://github.com/mixailab/usigan
공식 코드USIGAN: Unbalanced Self-Information Feature Transport for Weakly Paired Image IHC Virtual Staining
https://github.com/mjkwon2021/cat-net
공식 코드Learning JPEG Compression Artifacts for Image Manipulation Detection and Localization
https://github.com/mk-minchul/dcface
공식 코드DCFace: Synthetic Face Generation with Dual Condition Diffusion Model
https://github.com/mkocaoglu/CausalGAN
공식 코드CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training
https://github.com/ml-gsai/microdreamer
공식 코드MicroDreamer: Efficient 3D Generation in $\sim$20 Seconds by Score-based Iterative Reconstruction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.