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코드

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

https://github.com/alibaba/EasyCV

Deep Residual Learning for Image Recognition

https://github.com/alibaba/EasyNLP/tree/master/examples/text2image_generation

Taming Transformers for High-Resolution Image Synthesis

https://github.com/alililia/ascend_FaceAttribute

Deep Residual Learning for Image Recognition

https://github.com/alililia/ascend_SE-Net

Deep Residual Learning for Image Recognition

https://github.com/alililia/ascend_U-GAT-IT

U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation

https://github.com/alililia/ms_contrib/tree/main/ascend_vgg19

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/alililia/ms_contrib/tree/main/gpu_vgg19

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/alililia/ms_extend/tree/main/gpu_pix2pix

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/alililia/ms_extend/tree/main/gpu_resnet

Deep Residual Learning for Image Recognition

https://github.com/alililia/vision/tree/main/CycleGAN-MS

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

https://github.com/alimohammadiamirhossein/cora

Cora: Correspondence-aware image editing using few step diffusion

https://github.com/alisha17/benchmarks

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/alrojo/lasagne_residual_network

Deep Residual Learning for Image Recognition

https://github.com/alvarobartt/serving-pytorch-models

Deep Residual Learning for Image Recognition

https://github.com/amamov/vgg16-vs-resnet

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/aman-arya/Any-Body-Can-Dance

Everybody Dance Now

https://github.com/aman-garg0001/fruit-classifier

Deep Residual Learning for Image Recognition

https://github.com/amazon-research/long-short-term-transformer

Deep Residual Learning for Image Recognition

https://github.com/amazon-science/long-short-term-transformer

Deep Residual Learning for Image Recognition

https://github.com/ambityga/memsem

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/amdegroot/ssd.pytorch

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/amitaydr/Hackaton2018

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/amittiwary42/pix2pix-NNFL

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/amogh7joshi/engagement-detection

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/amogh7joshi/fer

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/amogh7joshi/plant-health-detection

Deep Residual Learning for Image Recognition

https://github.com/andersoncarlosfs/image_classification

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/andreasveit/densenet-pytorch

Deep Residual Learning for Image Recognition

https://github.com/andrewowens/multisensory

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/andrijdavid/clinical-brain-interface-2020

Deep Residual Learning for Image Recognition

https://github.com/angelvillar96/FaceEmoji

Deep Residual Learning for Image Recognition

https://github.com/anglihust/Unsupervised-vessel-segmentation

Adversarial Discriminative Domain Adaptation

https://github.com/anguyen8/sam

Deep Residual Learning for Image Recognition

https://github.com/anh-nn01/Satellite-Imagery-to-Map-Translation-using-Pix2Pix-GAN-framework

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/anibali/dsnt-pose2d

Deep Residual Learning for Image Recognition

https://github.com/aniket-patra-1998/FLOWER_RECOGNITION

Deep Residual Learning for Image Recognition

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