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/2023-MindSpore-4/Code15/tree/main/wangyikai/TokenFusion
https://github.com/2023-MindSpore-4/Code2/tree/main/dcgan
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/2023-MindSpore-4/Code6/tree/main/ReIDStrongBaseline
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/2023-MindSpore-4/Code7/tree/main/StarGAN
https://github.com/2023-MindSpore-4/Code7/tree/main/ssd_resnet34
https://github.com/2023-MindSpore-4/Code7/tree/main/ssd_resnet_34
https://github.com/2023-MindSpore-4/Code8/tree/main/CGAN
https://github.com/2023-MindSpore-4/Code9/tree/main/ResNet
https://github.com/2023-MindSpore-4/Code9/tree/main/VGG
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/2023-MindSpore-4/Code9/tree/main/WGAN
https://github.com/2024-MindSpore-1/Code10/tree/main/DRAN
DRAN: Detailed Region-Adaptive Normalization for Conditional Image Synthesis
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/cohere
https://github.com/2024-MindSpore-1/Code2/tree/main/wangyikai/EIP-mindspore
https://github.com/2024-MindSpore-1/Code4/tree/main/Arcface
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/2024-MindSpore-1/Code7/tree/main/CGAN
https://github.com/2024-MindSpore-1/Code7/tree/main/CTSDG
Image Inpainting for Irregular Holes Using Partial Convolutions
https://github.com/2024-MindSpore-1/Code7/tree/main/centernet_resnet101
https://github.com/2024-MindSpore-1/Code7/tree/main/centernet_resnet50_v1
https://github.com/2024-MindSpore-1/Code7/tree/main/dcgan
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/2xic/notebooks
https://github.com/394781865/insightface
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/3P2S/arcface
https://github.com/4ndyparr/DCGAN-landscapes
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/5yearsKim/Conditional-Normalizing-Flow
https://github.com/771979972/Paddle_pSp
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
https://github.com/771979972/paddle-SAM
Only a Matter of Style: Age Transformation Using a Style-Based Regression Model
https://github.com/89viper/Python-Deep_Video_Inpainting
https://github.com/9310gaurav/ali-pytorch
https://github.com/AI-Guru/agent_emotion_model
https://github.com/AI-for-Ocean-Science/ulmo
https://github.com/AIOSYM/dl-model-gallery
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/AIStream-Peelout/flow-forecast
Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting
https://github.com/AKASHKADEL/dcgan-celeba
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/AKASHKADEL/dcgan-mnist
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/AMD-AIG-AIMA/ReNeg
ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.