https://github.com/cutz-j/FDFtNet
공식 코드FDFtNet: Facing Off Fake Images using Fake Detection Fine-tuning Network
Papers with Code 아카이브
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/cv-rits/CoMoGAN
공식 코드https://github.com/cv-rits/manifest
공식 코드ManiFest: Manifold Deformation for Few-shot Image Translation
https://github.com/cvg/labelmaker
공식 코드ARKit LabelMaker: A New Scale for Indoor 3D Scene Understanding
https://github.com/cvi-szu/degstalk
공식 코드DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-Preserving Talking Face Synthesis
https://github.com/cvlab-stonybrook/ZoomLDM
공식 코드ZoomLDM: Latent Diffusion Model for multi-scale image generation
https://github.com/cvlab-stonybrook/fewshot-conditional-diffusion
공식 코드Conditional Generation from Unconditional Diffusion Models using Denoiser Representations
https://github.com/cvlab-stonybrook/large-image-diffusion
공식 코드Learned representation-guided diffusion models for large-image generation
https://github.com/cvlab-stonybrook/pathldm
공식 코드PathLDM: Text conditioned Latent Diffusion Model for Histopathology
https://github.com/cvlab-tohoku/DSEBImageInpainting
공식 코드https://github.com/cvmi-lab/speech2lip
공식 코드Speech2Lip: High-fidelity Speech to Lip Generation by Learning from a Short Video
https://github.com/cvmi-lab/syntheticdata
공식 코드Is synthetic data from generative models ready for image recognition?
https://github.com/cvrl/forensic-iris-image-synthesis
공식 코드https://github.com/cwkx/GON
공식 코드https://github.com/cx921003/UPG-GAN
공식 코드https://github.com/cxjyxxme/pix2pixSC
공식 코드Example-Guided Style Consistent Image Synthesis from Semantic Labeling
https://github.com/cxxgtxy/usp
공식 코드USP: Unified Self-Supervised Pretraining for Image Generation and Understanding
https://github.com/cxy000000/IPR-RLDF
공식 코드Learning from Mistakes: Iterative Prompt Relabeling for Text-to-Image Diffusion Model Training
https://github.com/cyang-cityu/MetaCorrection
공식 코드https://github.com/cyclomon/3dbraingen
공식 코드Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networks
https://github.com/cyclomon/unsb
공식 코드Unpaired Image-to-Image Translation via Neural Schrödinger Bridge
https://github.com/cyq373/ZUNIT
공식 코드https://github.com/cyrilsterling/textgen
공식 코드How Control Information Influences Multilingual Text Image Generation and Editing?
https://github.com/czczup/block-shuffle
공식 코드Block Shuffle: A Method for High-resolution Fast Style Transfer with Limited Memory
https://github.com/czg1225/code
공식 코드Collaborative Decoding Makes Visual Auto-Regressive Modeling Efficient
https://github.com/da-group-pku/magic-1-for-1
공식 코드Magic 1-For-1: Generating One Minute Video Clips within One Minute
https://github.com/da03/markup2im
공식 코드https://github.com/dae-plz/re-gips
공식 코드Novel-view X-ray Projection Synthesis through Geometry-Integrated Deep Learning
https://github.com/dahoas/edm
공식 코드DFU: scale-robust diffusion model for zero-shot super-resolution image generation
https://github.com/daixiangzi/var-clip
공식 코드VAR-CLIP: Text-to-Image Generator with Visual Auto-Regressive Modeling
https://github.com/daizigege/Identity_hider
공식 코드Seeing is not Believing: An Identity Hider for Human Vision Privacy Protection
https://github.com/damaggu/tadp
공식 코드https://github.com/damo-vilab/composer
공식 코드Composer: Creative and Controllable Image Synthesis with Composable Conditions
https://github.com/danelee2601/supervised-fcn
공식 코드Vector Quantized Time Series Generation with a Bidirectional Prior Model
https://github.com/daniela997/filmdamagesimulator
공식 코드Simulating analogue film damage to analyse and improve artefact restoration on high-resolution scans
https://github.com/danielroich/PTI
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.