https://github.com/MindSpore-scientific/code-5/tree/main/SGDR
SGDraw: Scene Graph Drawing Interface Using Object-Oriented Representation
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/MindSpore-scientific/code-5/tree/main/Scalable-Sharpness-Aware-Minimization
https://github.com/MindSpore-scientific/code-8/tree/main/SING
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/MinfengZhu/DM-GAN
DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-to-Image Synthesis
https://github.com/Mingtzge/2019-CCF-BDCI-OCR-MCZJ-OCR-IdentificationIDElement
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/MinkaiXu/SobolevWassersteinGAN
https://github.com/MioChiu/ResNet_TensorFlow
https://github.com/MisaOgura/flashtorch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Misaka9468/CloudTransfer-daytime2sunset-CycleGAN-Pytorch
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/MishaLaskin/vq-vae
https://github.com/MishaLaskin/vqvae
https://github.com/Mmhmmmmm/ss-infogan-pytorch
https://github.com/Mmhmmmmm/ylg_pytorch
https://github.com/Mmhmmmmm/ylg_pytorvh
https://github.com/Mohammad-Rahmdel/WassersteinGAN-Tensorflow
https://github.com/Mohammad-Rahmdel/WassersteinGAN-Tensorflow-
https://github.com/Mohanned-Elkholy/BigGan-model-inversion-with-image-transformations
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://github.com/Mohanned-Elkholy/ResNet-GAN
https://github.com/Moldoteck/lama
Resolution-robust Large Mask Inpainting with Fourier Convolutions
https://github.com/Montia/bw2color
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/MoonInTheRiver/DiffSinger
https://github.com/Morefre/Understanding-Generalizability-of-Diffusion-Models-Requires-Rethinking-the-Hidden-Gaussian-Structure
Elucidating the Design Space of Diffusion-Based Generative Models
https://github.com/MoritzKappel/HF-NHMT
High-Fidelity Neural Human Motion Transfer from Monocular Video
https://github.com/MorningStarOvO/dissect
Understanding the Role of Individual Units in a Deep Neural Network
https://github.com/MoustafaMeshry/draw
https://github.com/MrRiahi/Convolutional-Neural-Networks-Tensorflow
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Mrnoorsingh/image-caption
https://github.com/Muhammad-Huzaifaa/ObjectCompose
LANCE: Stress-testing Visual Models by Generating Language-guided Counterfactual Images
https://github.com/Mukosame/Anime2Sketch
Adversarial Open Domain Adaptation for Sketch-to-Photo Synthesis
https://github.com/Mulham91/Multi-Spectral-Image-Synthesis-for-Crop-Weed-Segmentation-in-Precision-Farming
Multi-Spectral Image Synthesis for Crop/Weed Segmentation in Precision Farming
https://github.com/Murali81/InfoGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/MurreyCode/wavegan
https://github.com/MustafaMustafa/cosmoGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/MustafaMustafa/dcgan-tf-benchmark
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/NASA-NeMO-Net/NeMO-Net
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/NDCHIRO/Cartoonizer
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.