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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/kw01sg/crda

DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation

https://github.com/kwantommy/fgvc6-kaggle-cassava-classification

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/kwotsin/TensorFlow-ENet

Deep Residual Learning for Image Recognition

https://github.com/kyle-dorman/bayesian-neural-network-blogpost

Deep Residual Learning for Image Recognition

https://github.com/kylelo/roofdiffusion

Palette: Image-to-Image Diffusion Models

https://github.com/kynkaat/improved-precision-and-recall-metric

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/kzkymur/transition

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/lab-midas/med_segmentation

Deep Residual Learning for Image Recognition

https://github.com/labmlai/annotated_deep_learning_paper_implementations

Deep Residual Learning for Image Recognition

https://github.com/laclark/cuneiform_lineart

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/lancerane/Adversarial-domain-adaptation

Unsupervised Domain Adaptation by Backpropagation

https://github.com/layumi/DG-Net

Joint Discriminative and Generative Learning for Person Re-identification

https://github.com/layumi/Person_reID_baseline_pytorch

Joint Discriminative and Generative Learning for Person Re-identification

https://github.com/leebebeto/pytorch-implementation

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/leehomyc/cyclegan-1

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/leemathew1998/GradientWeight

Deep Residual Learning for Image Recognition

https://github.com/leemathew1998/RG

Deep Residual Learning for Image Recognition

https://github.com/leenaali1114/Hierarchical-Image-Clustering---Unsupervised-Learning

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/leffff/FlowModels

Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

https://github.com/leffff/flow-matching-experiments

Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

https://github.com/leob03/E2E_Object_Pose_Estimator

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/leongatys/DeepTextures

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/leongatys/PytorchNeuralStyleTransfer

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/lh-wang/ACC

Virtual to Real Reinforcement Learning for Autonomous Driving

https://github.com/lianqing11/pycda

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach

https://github.com/libreai/neural-painters-x

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/lightning-uq-box/lightning-uq-box

Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging

https://github.com/linainsaf/RESNET50_PNEUMONIA_DETECTION

Deep Residual Learning for Image Recognition

https://github.com/linxi159/GAN-training-tricks

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/linxi159/Tips-and-tricks-to-train-GANs

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/liorfrenkel1992/SB-GAN

Semantic Bottleneck Scene Generation

https://github.com/liqunchen0606/triangle-gan

Triangle Generative Adversarial Networks

https://github.com/liuliuliu11/pixel2style2pixel-liu

Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation

https://github.com/liuyao12/ConvNets-PDE-perspective

Deep Residual Learning for Image Recognition

https://github.com/liuzeyuMr/CycleGAN-TensorFlow-master

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/lixihan/Resnet

Deep Residual Learning for Image Recognition

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.