Papers with Code 아카이브

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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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하위 태스크

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평가표

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보존된 평가 기록

5

코드

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

https://github.com/zb12138/sph3d

Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds

https://github.com/zb12138/sph3dR

Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds

https://github.com/zbp-xxxp/R-Drop-Paddle

R-Drop: Regularized Dropout for Neural Networks

https://github.com/zcemycl/Matlab-GAN

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/zdcuob/cnn_graph-

Spectral Networks and Locally Connected Networks on Graphs

https://github.com/ze1gades/diary

SkinAugment: Auto-Encoding Speaker Conversions for Automatic Speech Translation

https://github.com/zeka-io/selfi-to-anime

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

https://github.com/zeka-io/selfie-to-anime

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

https://github.com/zengzhaoyang/Weak_Detection

R-FCN: Object Detection via Region-based Fully Convolutional Networks

https://github.com/zengzhaoyang/trident

R-FCN: Object Detection via Region-based Fully Convolutional Networks

https://github.com/zepx/pytorch-weight-prune

Compression of Neural Machine Translation Models via Pruning

https://github.com/zer0sh0t/artificial_intelligence/tree/master/transformer

Attention Is All You Need

https://github.com/zgxsin/lidar_camera_calibration

LiDAR-Camera Calibration using 3D-3D Point correspondences

https://github.com/zhang-huihui/git-repository

Attention Is All You Need

https://github.com/zhang0jhon/AttentionOCR

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/zhangbo2008/best_pytorch_transformer

Attention Is All You Need

https://github.com/zhangtj1996/lookahead-sgd-adam-rmsprop-

Lookahead Optimizer: k steps forward, 1 step back

https://github.com/zhangyaoyuan/GAN-Simplification

Using the Output Embedding to Improve Language Models

https://github.com/zhangzibin/char-rnn-chinese

Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks

https://github.com/zhaojingjing713/hpr

Aligning Pretraining for Detection via Object-Level Contrastive Learning

https://github.com/zhaopku/LSTM-Jump-for-text-skimming

Learning to Skim Text

https://github.com/zhaozhongch/yolact_ros

YOLACT: Real-time Instance Segmentation

https://github.com/zhawhjw/yolact-interpret

YOLACT: Real-time Instance Segmentation

https://github.com/zhdbwe/Paper-DailyReading

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

https://github.com/zhengziqiang/P2C

Unpaired Photo-to-Caricature Translation on Faces in the Wild

https://github.com/zhhchen4njit/yolact

YOLACT: Real-time Instance Segmentation

https://github.com/zhijing-jin/GEC_papers

Improving Grammatical Error Correction with Machine Translation Pairs

https://github.com/zhong110020/TensorFlow_TCN

An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

https://github.com/zhong110020/Tensorflow-TCN

An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

https://github.com/zhong110020/keras-tcn

An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

https://github.com/zhong110020/pytorch_TCN

An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

https://github.com/zhou059/w266-project

Quasi-Recurrent Neural Networks

https://github.com/zhouZhouYUI/UGATIT_paddle

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

https://github.com/zhuo931077127/CycleGAN_practice

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

https://github.com/zhusiling/TecoGAN

Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation

https://github.com/zichunhao/lgn-autoencoder

A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups

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