https://github.com/khush3/GAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Papers with Code 아카이브
Image Generation
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- 평가표
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- 보존된 평가 기록
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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/kiban/models
https://github.com/kidist-amde/biggan-pytorch
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://github.com/kimdanni/DiffMAE
https://github.com/kimhaggie/YBIGTA_haggie
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/kingcheng2000/GAN
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/kingcong/SinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/kingcong/gpu_FaceAttribute
https://github.com/kingcong/gpu_SE-Net
https://github.com/kingcong/resnet
https://github.com/kingoflolz/mesh-transformer-jax
ZeRO: Memory Optimizations Toward Training Trillion Parameter Models
https://github.com/kiranu1024/deep-learning
https://github.com/kirbiyik/caption-it
https://github.com/kishanAk21/Skin-Cancer-Image-Classification
https://github.com/kishorkuttan/traffic_net
https://github.com/kitliu5/Stanford-CS236-Project
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/kiyohiro8/SelfAttentionGAN
https://github.com/kiyohiro8/pytorch-unpaired-image-to-image-translation
Attention-Guided Generative Adversarial Networks for Unsupervised Image-to-Image Translation
https://github.com/kjm1559/ADAE_LSTM_Autoencoder
https://github.com/kkleidal/gatedpixelcnnpytorch
https://github.com/kklemon/bgan-pytorch
https://github.com/kklemon/gon-pytorch
https://github.com/km1414/CNN-models
https://github.com/km3888/zeroforge
Neural Voxel Renderer: Learning an Accurate and Controllable Rendering Tool
https://github.com/kmbmjn/disappearance_of_timestep_embedding
The Disappearance of Timestep Embedding in Modern Time-Dependent Neural Networks
https://github.com/kmkwon94/ainized-ganilla-dev
GANILLA: Generative Adversarial Networks for Image to Illustration Translation
https://github.com/kmzzhang/nbi
https://github.com/kohakublueleaf/kgen
TIPO: Text to Image with Text Presampling for Prompt Optimization
https://github.com/kok202/ApolloGAN
https://github.com/koles289/udacity-Image-Captioning
https://github.com/komeiharada/learn_DL_together
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/komiya-m/MirrorGAN
StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks
https://github.com/komyeongjin/specdiff-gan
Tackling the Generative Learning Trilemma with Denoising Diffusion GANs
https://github.com/konny0311/vgg
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/konstantinos-p/image_classification_SOTA
https://github.com/kornia/kornia/blob/e4a54c2d4fd9b8eaa88dec50061e33f7aee5d959/kornia/filters/blur_pool.py
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.