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/ashkanpakzad/atn
Learning from Simulated and Unsupervised Images through Adversarial Training
https://github.com/ashu170292/GANs
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/ashura1234/soccer-line-detection
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/ashushekar/VGG16
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ashutosh1919/FaceGenerationStyleGAN
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ashutosh1919/mdp-diffusion
MDP: A Generalized Framework for Text-Guided Image Editing by Manipulating the Diffusion Path
https://github.com/asierae/VoiceDCGAN
https://github.com/asif-hanif/baple
A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models
https://github.com/asiltureli/gan-in-colab
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/asleepytree/eb-cache
$Δ$-DiT: A Training-Free Acceleration Method Tailored for Diffusion Transformers
https://github.com/asmerdov/DataCollectionSystem
https://github.com/asprenger/keras_acgan
https://github.com/asprenger/keras_fc_densenet
https://github.com/asraman9792/Generative-Adversarial-Networks-implementation-of-KERAS
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/astoff/dog-breed-app
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/astoycos/Mini_Project2
https://github.com/astro-informatics/rcGAN
A Regularized Conditional GAN for Posterior Sampling in Image Recovery Problems
https://github.com/astrodeepnet/diffusion4astro
https://github.com/asyrovprog/cs230project
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/atharv6/Image-Captioning
https://github.com/atinghosh/Adverserial-VAE
Generating Images with Perceptual Similarity Metrics based on Deep Networks
https://github.com/attm/tensorflow_advent
ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
https://github.com/atun-MEI/AI-Course-Final
https://github.com/aurelien-peden/Deep-Learning-paper-implementations
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/austinxy/giraffehd
https://github.com/autonomobil/SCDND-P2_Traffic-Sign-Classifier
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/autonomousvision/campari
https://github.com/autonomousvision/frequency_bias
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/autonomousvision/stylegan-xl
https://github.com/avakong/stylegan-test
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/avalonstrel/GatedConvolution
https://github.com/avalonstrel/GatedConvolution_pytorch
https://github.com/avellal14/AI-for-Art
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/avihu111/viewpoint_disentanglement
https://github.com/avisekiit/wacv_2019
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.