https://github.com/karlchahine/neural-cover-selection-for-image-steganography
Progressive Growing of GANs for Improved Quality, Stability, and Variation
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/karolrogozinski/cern_alice_fast_sim_corrvae
https://github.com/karoly-hars/GAN_image_colorizing
https://github.com/karpathy/pytorch-made
https://github.com/karynaur/High-Fidelity-Pluralistic-ICT
High-Fidelity Pluralistic Image Completion with Transformers
https://github.com/karynaur/ICT
High-Fidelity Pluralistic Image Completion with Transformers
https://github.com/kaseris/ILSVRCPlus
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/katychou/generative01
https://github.com/kauravin/Neural-Visual-Image-Caption-Generation-using-RNNs-and-LSTMs
https://github.com/kaustubh1955/pixelDeconvolution
https://github.com/kaziwasaleh/mask-guided
TextureGAN: Controlling Deep Image Synthesis with Texture Patches
https://github.com/kbpi314/iclust_paper
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/kclip/localized-adaptive-risk-control
https://github.com/kdhRick2222/Exposure-slot
Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse Problems
https://github.com/keillernogueira/exploit-cnn-rs
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/kelvin-jose/dcgan_mxnet
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/kenanidris/deoldify
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/kenkai21/Image_Captioning
https://github.com/keonlee9420/DiffGAN-TTS
https://github.com/keonlee9420/DiffSinger
https://github.com/keonlee9420/VAENAR-TTS
https://github.com/keras-team/keras-applications
https://github.com/keras-team/keras-cv
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/keras-team/keras-io/blob/master/examples/generative/ddim.py
https://github.com/keras-team/keras-io/blob/master/examples/generative/gan_ada.py
https://github.com/keras-team/keras-io/blob/master/examples/generative/stylegan.py
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/kerryvernebegeman/Kerry-Verne-Begeman
https://github.com/kevin-thu/diffmorpher
DiffMorpher: Unleashing the Capability of Diffusion Models for Image Morphing
https://github.com/kevin851066/Deep_Learning_for_Computer_Vision
https://github.com/kevinmusgrave/pytorch-adapt
https://github.com/kggreene/kggreene.github.io
An Infinite Parade of Giraffes: Expressive Augmentation and Complexity Layers for Cartoon Drawing
https://github.com/kh-kim/simple-nmt
https://github.com/kh12lee/kh_lee
https://github.com/khangt1k25/MADE
https://github.com/khawajatalhahaseeb/Class-Activation-Map-Keras
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/khurram702/StyleBasedGAN
Very Deep Convolutional Networks for Large-Scale Image Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.