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/mansimov/text2image
https://github.com/mantonios107/attrs-fr-embs
https://github.com/manyids2/kornia
Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
https://github.com/marccoru/locationencoder
Implicit Neural Representations with Periodic Activation Functions
https://github.com/marcojira/fld
Improved Precision and Recall Metric for Assessing Generative Models
https://github.com/marcojira/fls
Improved Precision and Recall Metric for Assessing Generative Models
https://github.com/markmaxt/VideoSR
https://github.com/marload/ConvNets-TensorFlow2
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/marload/GANs-TensorFlow2
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/marload/TensorFlow2-GANs
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/martin220485/everybody_dance_now_pytorch
https://github.com/martinarjovsky/WassersteinGAN
https://github.com/martinjurkovic/syntherela
Generating Realistic Synthetic Relational Data through Graph Variational Autoencoders
https://github.com/maruru-hub/PartialConv-paddle
Image Inpainting for Irregular Holes Using Partial Convolutions
https://github.com/mashaan14/ADDA
https://github.com/mashaan14/ADDA-toy
https://github.com/mashaan14/DANN-toy
https://github.com/masoudnick/brain-tumor-mri-classification
https://github.com/mastanimahdi/pix2pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/matakshay/Neural_Image_Caption_Generator
https://github.com/matanr/MLography
An End-to-End Computer Vision Methodology for Quantitative Metallography
https://github.com/mathilde-b/SRDA
A Curriculum Domain Adaptation Approach to the Semantic Segmentation of Urban Scenes
https://github.com/matjazmav/fri-2021-ibb-seminar
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/matlab-deep-learning/pix2pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/matsagad/mres-project
Practical and Asymptotically Exact Conditional Sampling in Diffusion Models
https://github.com/matt-bendel/pcagan
A Regularized Conditional GAN for Posterior Sampling in Image Recovery Problems
https://github.com/matt-roz/progressive_growing_of_gans_tensorflow_2
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/matteo-bastico/coupledlaplacian
https://github.com/matteodalessio/CycleGAN-android
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/mattiasxu/VQVAE-2
https://github.com/mattiasxu/Video-VQVAE
https://github.com/mattya/chainer-DCGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/maum-ai/wavegrad2
https://github.com/mauriceweiler/MobiusCNNs
https://github.com/maxbeauchamp/2020a_IMT_SSH_mapping_NATL60
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.