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/am-sirdaniel/Image-Captioning-Model
https://github.com/amamov/vgg16-vs-resnet
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/aman-arya/Any-Body-Can-Dance
https://github.com/aman-garg0001/fruit-classifier
https://github.com/amanjaiswal73892/change_my_pet
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://github.com/amanshenoy/pose-invariant-face-recognition
https://github.com/amaranth819/dcgan-cifar10-pytorch
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/amazon-research/long-short-term-transformer
https://github.com/amazon-science/long-short-term-transformer
https://github.com/amazon-science/tabsyn
https://github.com/amberkboulton/StyleGAN3
https://github.com/ambityga/memsem
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/amdegroot/ssd.pytorch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ameraner/dsen2-cr
https://github.com/amir7d0/InfoGAN
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
https://github.com/amirhb29/StyleGAN2_Style-Mixing
https://github.com/amiryanj/socialways
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
https://github.com/amitaydr/Hackaton2018
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/amittiwary42/pix2pix-NNFL
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/amogh7joshi/engagement-detection
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/amogh7joshi/fer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/amogh7joshi/plant-health-detection
https://github.com/amoghadishesha/W-gan-Simple
https://github.com/amurthy1/dagan_torch
https://github.com/amyzhao11/GANBrain
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/anandaltekar/movie-poster-generator
https://github.com/anandchandra50/deep-space
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/ananyahjha93/challenges-in-disentangling
Challenges in Disentangling Independent Factors of Variation
https://github.com/ananyahjha93/cycle-consistent-vae
Challenges in Disentangling Independent Factors of Variation
https://github.com/andersoncarlosfs/image_classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/andreamazzitelli/ProjectNN
Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model
https://github.com/andrearama/Deep-Auxiliary-Classifier-GAN
https://github.com/andreas128/NTIRE21_Learning_SR_Space
SRFlow: Learning the Super-Resolution Space with Normalizing Flow
https://github.com/andreasfloros/diffusion
https://github.com/andreasjansson/fmsynth
Implicit Neural Representations with Periodic Activation Functions
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.