https://github.com/faddey-w/face-swap
FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
Papers with Code 아카이브
Image Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 보존된 평가 기록
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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/fai07600521/final-project
https://github.com/faizaan09/caption-to-image-generation
https://github.com/fanchenyou/deepdiary
https://github.com/fangyiyu/gnpassgan
https://github.com/fangzy96/tabcutmix
https://github.com/fashn-AI/tryondiffusion
Progressive Distillation for Fast Sampling of Diffusion Models
https://github.com/fastai/fastai
https://github.com/fastforwardlabs/deepad
https://github.com/fchollet/deep-learning-models
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/fdasilva59/Pix2Pix-Kubeflow-Demo
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/feipanir/intrada
Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision
https://github.com/feixuetuba/inpainting
Image Inpainting for Irregular Holes Using Partial Convolutions
https://github.com/feixuetuba/inpating
Image Inpainting for Irregular Holes Using Partial Convolutions
https://github.com/felixgwu/img_classification_pk_pytorch
https://github.com/fengpings/Two-branch-Document-Recapture
Two-branch Multi-scale Deep Neural Network for Generalized Document Recapture Attack Detection
https://github.com/fengredrum/cnn-xla
https://github.com/fhshen2022/prunerepaint
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/fikratgasimov/Generative_Adversial
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/filipbasara0/simple-diffusion
https://github.com/fk798/dcgan
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/flagopen/flagscale
https://github.com/flavioschneider/ml_papers_presentations
https://github.com/flynn-chen/faceforensics_benchmark
FaceForensics++: Learning to Detect Manipulated Facial Images
https://github.com/fmorenovr/Semi-Supervised-Learning_with_GAN_Keras
https://github.com/fmu2/NICE
https://github.com/fmu2/realNVP
https://github.com/foamliu/Gaze-Estimation
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/foamliu/InsightFace-PyTorch
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/foamliu/InsightFace-v2
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/foamliu/InsightFace-v3
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/foamliu/StyleGAN-PyTorch
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/foivospar/Arc2Face
https://github.com/foundation-model-research/DiffLens
Dissecting and Mitigating Diffusion Bias via Mechanistic Interpretability
https://github.com/fourmi1995/IronsegExperiment-PixelDCL
https://github.com/francis-kang/handson-unsupervised-learning
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.