https://github.com/iyttor/GPNN
Drop the GAN: In Defense of Patches Nearest Neighbors as Single Image Generative Models
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/j-min/LayoutBench-COCO
Diagnostic Benchmark and Iterative Inpainting for Layout-Guided Image Generation
https://github.com/j-nowak/covid-19
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/j-towns/vdvae-jax
Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images
https://github.com/j-void/ISL_v2v
https://github.com/j3soon/arxiv-utils
https://github.com/ja3067/wgan-pytorch
https://github.com/jackaduma/CycleGAN-VC2
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/jackgoffinet/ffjord-lite
FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models
https://github.com/jacklishufan/Reflect-DiT
https://github.com/jackonelli/mcmc_corr_score_diffusion
Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC
https://github.com/jackyjsy/ACWGAN
https://github.com/jackyjsy/SGGAN
Segmentation Guided Image-to-Image Translation with Adversarial Networks
https://github.com/jacobgil/keras-dcgan
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/jacobgil/pytorch-grad-cam
Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs
https://github.com/jacobjinkelly/easy-neural-ode
FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models
https://github.com/jadohu/LANTERN
LANTERN: Accelerating Visual Autoregressive Models with Relaxed Speculative Decoding
https://github.com/jaingaurav3/GAN-Hacks
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/jam-ing/DALLE-pytorch
https://github.com/james-oldfield/mmoe
Parts of Speech-Grounded Subspaces in Vision-Language Models
https://github.com/james77777778/keras-image-models
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jameskokoska/rvl
https://github.com/jamesli1618/Obj-GAN
Object-driven Text-to-Image Synthesis via Adversarial Training
https://github.com/jameswang287/Car-Detection
https://github.com/janZub-AI/EmotionDetectionVGG
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/janesjanes/Pytorch-TextureGAN
TextureGAN: Controlling Deep Image Synthesis with Texture Patches
https://github.com/janfreyberg/pytorch-revgrad
https://github.com/jankrepl/mildlyoverfitted
Implicit Neural Representations with Periodic Activation Functions
https://github.com/jantic/DeOldify
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/jaredaevans/FishGAN
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/jarek-pawlowski/advanced-machine-learning
https://github.com/jason9693/cycleGAN-tensorflow-v2
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/jay-z007/Text-to-Image-Synthesis
https://github.com/jayChung0302/DeepFilter
https://github.com/jaywalnut310/Vector-Quantized-Autoencoders
https://github.com/jazzsaxmafia/show_and_tell.tensorflow
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.