https://github.com/buddly27/stylish
Instance Normalization: The Missing Ingredient for Fast Stylization
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/buganart/stylegan2-ada-pytorch
https://github.com/bunny98/Text-to-Image-Using-GAN
https://github.com/buoyancy99/diffusion-forcing
https://github.com/buriburisuri/ac-gan
https://github.com/buriburisuri/timeseries_gan
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
https://github.com/buriedms/AdaptSegNet-Paddle
Learning to Adapt Structured Output Space for Semantic Segmentation
https://github.com/bxck75/piss-ant-pix2pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/bytedance/MVDream-threestudio
https://github.com/bytedance/cascadev
SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis
https://github.com/bytedance/imagedream
ImageDream: Image-Prompt Multi-view Diffusion for 3D Generation
https://github.com/bytedance/mvdream
https://github.com/bytedance/x-dyna
Adding Conditional Control to Text-to-Image Diffusion Models
https://github.com/c1ph3rr/Deep-Residual-Learning-for-Image-Recognition
https://github.com/c1ph3rr/Very-Deep-Convolutional-Networks-For-Large-Scale-Image-Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/cachett/DCGANandCAE
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/caffe-mocha/dcgan-pytorch-cifar10
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/caiyuanhao1998/PNGAN
Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial Training
https://github.com/cal-app/slow-NST
https://github.com/calico/scnym
https://github.com/calmisential/TensorFlow2.0_Image_Classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/calmisential/TensorFlow2.0_ResNet
https://github.com/cameronchoi/r3det-copy
https://github.com/candicexxx-droid/PIC16B---GAN-Project-
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/cant12/MammoGan
https://github.com/canturan10/satellighte
https://github.com/caojoshua/Plastic-Recyclability
https://github.com/caoquanjie/ADDA-master
https://github.com/caotians1/OD-test-master
https://github.com/capstonecs42/GeNeVA_datasets_dev
Tell, Draw, and Repeat: Generating and Modifying Images Based on Continual Linguistic Instruction
https://github.com/captain-pool/DC-GAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/carlosb1/upc-aidl-19-team4
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/carlosh93/sfr_hb-irit-uis
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/carnotaur/CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/carolgithubv1/convnets-keras
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/carpedm20/BEGAN-tensorflow
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.