Papers with Code 아카이브
Image Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 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/liqunchen0606/triangle-gan
https://github.com/litalby1/make-it-count
Make It Count: Text-to-Image Generation with an Accurate Number of Objects
https://github.com/liuch37/reground
ReGround: Improving Textual and Spatial Grounding at No Cost
https://github.com/liuf1990/CFSM
Controllable and Guided Face Synthesis for Unconstrained Face Recognition
https://github.com/liuff19/physics3d
Physics3D: Learning Physical Properties of 3D Gaussians via Video Diffusion
https://github.com/liuliuliu11/pixel2style2pixel-liu
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
https://github.com/liuxiaoyu1104/smartcontrol
IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models
https://github.com/liuyao12/ConvNets-PDE-perspective
https://github.com/liuzeyuMr/CycleGAN-TensorFlow-master
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/liuzhengzhe/exim
DiffEdit: Diffusion-based semantic image editing with mask guidance
https://github.com/livingbio/MakeItTalk
https://github.com/liviniuk/GANwriting
GANwriting: Content-Conditioned Generation of Styled Handwritten Word Images
https://github.com/lixihan/Resnet
https://github.com/lixixibj/forecasting-with-time-series-imaging
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/lixixibj/tsImage-forecasting
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/liyinqi/un2clip
Hierarchical Text-Conditional Image Generation with CLIP Latents
https://github.com/liyuantsao/flowsr-lp
SRFlow: Learning the Super-Resolution Space with Normalizing Flow
https://github.com/lizuoyue/sat2scene
Sat2Scene: 3D Urban Scene Generation from Satellite Images with Diffusion
https://github.com/ljy-hy/mentormix_pytorch
https://github.com/lkeonwoo94/DL_cv-mdt_NVIDIA_Cert_Course_StudyPI
https://github.com/lkhphuc/Anomaly-BiGAN
https://github.com/lkhphuc/Anomaly-XRay-GANs
https://github.com/lllyasviel/fooocus
Improving Sample Quality of Diffusion Models Using Self-Attention Guidance
https://github.com/lllyasviel/omost
https://github.com/llvictorll/AS
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/lmnt-com/diffwave
https://github.com/lmnt-com/wavegrad
https://github.com/locuslab/deq-ddim
https://github.com/locuslab/get
https://github.com/locuslab/impsq
Implicit Neural Representations with Periodic Activation Functions
https://github.com/lokhande-vishnu/deepcg
Constrained Deep Learning using Conditional Gradient and Applications in Computer Vision
https://github.com/lonce/sonyGanFork
https://github.com/lonePatient/EvoNorms_PyTorch
https://github.com/longchr123/insightface-analysis
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/longjj/Caffe-SGDR
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.