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/leffff/flow-matching-experiments
Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
https://github.com/lelechen63/stylegannerf
https://github.com/leminhbinh0209/add
Deferred Neural Rendering: Image Synthesis using Neural Textures
https://github.com/lengstrom/fast-style-transfer
Instance Normalization: The Missing Ingredient for Fast Stylization
https://github.com/lenz3000/ffjord-path
FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models
https://github.com/leoHeidel/vae-gan-tf2
https://github.com/leob03/E2E_Object_Pose_Estimator
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/leob03/Image_captionning
https://github.com/leognha/PyTorch-FID-score
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/resnet_family
https://github.com/leongatys/DeepTextures
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/leongatys/PytorchNeuralStyleTransfer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/lewisluk/PartialConvUnet
Image Inpainting for Irregular Holes Using Partial Convolutions
https://github.com/lh-wang/ACC
Virtual to Real Reinforcement Learning for Autonomous Driving
https://github.com/li012589/NeuralRG
https://github.com/liang-hou/ssgan-la
https://github.com/lianqing11/pycda
https://github.com/lianruizuo/haca3
Optimal operating MR contrast for brain ventricle parcellation
https://github.com/liaoguiqiu/DeepContour
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/liaoguiqiu/Multiscal_shifting
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/liaopeiyuan/pndm
https://github.com/libreai/neural-painters-x
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/lifeitech/fce-2d
https://github.com/lightning-uq-box/lightning-uq-box
https://github.com/lil-lab/chalet
Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction
https://github.com/lil-lab/ciff
Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction
https://github.com/lilianweng/unified-gan-tensorflow
https://github.com/linainsaf/RESNET50_PNEUMONIA_DETECTION
https://github.com/linghu8812/pytorch_insightface
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/linusnie/diffcd
Implicit Neural Representations with Periodic Activation Functions
https://github.com/linweiii/backdoordm
ConceptPrune: Concept Editing in Diffusion Models via Skilled Neuron Pruning
https://github.com/linxi159/GAN-training-tricks
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/linxi159/Tips-and-tricks-to-train-GANs
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/linxi159/dcgan_code
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/linzhiqiu/t2i_metrics
GenAI-Bench: Evaluating and Improving Compositional Text-to-Visual Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.