Papers with Code 아카이브
Image Reconstruction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 5
- 평가표
- 8
- 보존된 평가 기록
- 35
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/genforce/idinvert_pytorch
https://github.com/georg-wolflein/hoechstgan
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/gerardoglz/GANs
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/glouppe/info8010-deep-learning
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/gongenhao/GANCS
Deep Generative Adversarial Networks for Compressed Sensing Automates MRI
https://github.com/guanhuaw/mirtorch
https://github.com/hahahappyboy/GANForCartoon
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/hanyoseob/roi-ct-cnn
One Network to Solve All ROIs: Deep Learning CT for Any ROI using Differentiated Backprojection
https://github.com/haratzouvara/image_restoration
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/hariv/pix2pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/harshitbhushan68/Pix-2-Pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/hellopipu/RefineGAN
Compressed Sensing MRI Reconstruction using a Generative Adversarial Network with a Cyclic Loss
https://github.com/hero9968/pix2pix-tensorflow
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/hj-harry/ddip3d
Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems
https://github.com/hj-harry/diffusionmbir
Solving 3D Inverse Problems using Pre-trained 2D Diffusion Models
https://github.com/hjahan58/framing-u-net
Framing U-Net via Deep Convolutional Framelets: Application to Sparse-view CT
https://github.com/hyn2028/llm-cxr
https://github.com/icon-lab/pflsynth
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/illume-unified-mllm/ILLUME_plus
ILLUME+: Illuminating Unified MLLM with Dual Visual Tokenization and Diffusion Refinement
https://github.com/iodncookie/my_loss_monodepth_master
Unsupervised Monocular Depth Estimation with Left-Right Consistency
https://github.com/isennkubilay/monodepth2_tf
https://github.com/isrugeek/semcolour
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/itsuki8914/pix2pix-automaticColorization
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/ivankunyankin/pix2pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/jackaduma/CycleGAN-VC2
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/jaejun-yoo/TDDIP
https://github.com/jaingaurav3/GAN-Hacks
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/janesjanes/Pytorch-TextureGAN
TextureGAN: Controlling Deep Image Synthesis with Texture Patches
https://github.com/janisgp/Sampling-free-Epistemic-Uncertainty
Unsupervised Monocular Depth Estimation with Left-Right Consistency
https://github.com/jiang-du/Perceptual-CS
Fully Convolutional Measurement Network for Compressive Sensing Image Reconstruction
https://github.com/jingwei-bao/t-invblocks
Plug-and-Play Tri-Branch Invertible Block for Image Rescaling
https://github.com/jingyunliang/vrt
https://github.com/jiz322/Reconstruction-Attacks
Targeted Image Reconstruction by Sampling Pre-trained Diffusion Model
https://github.com/jkvt2/Latent-Pose-Descriptors
https://github.com/jmrf/dcscn-super-resolution
Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.