Papers with Code 아카이브
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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/SpikeKing/My-Bert
https://github.com/StatNLP/joeynmt
https://github.com/StatsDLMathsRecomSys/Inductive-representation-learning-on-temporal-graphs
https://github.com/StatsDLMathsRecomSys/Self-attention-with-Functional-Time-Representation-Learning
https://github.com/SteffenEger/ocr_spelling_deuparl
A Large-Scale Comparison of Historical Text Normalization Systems
https://github.com/Stevan-Zhuang/image-domain-transfer
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
https://github.com/StevenLai1994/AM_and_LM
https://github.com/StillKeepTry/Transformer-PyTorch
https://github.com/StoneGH/bert
https://github.com/StrikerCC/Teaser-plusplus-testing
A Polynomial-time Solution for Robust Registration with Extreme Outlier Rates
https://github.com/StuartCHAN/KARL
https://github.com/Sudhir11292rt/myBDL
Bidirectional Learning for Domain Adaptation of Semantic Segmentation
https://github.com/Sugarc0de/image_captioning
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/SummerHuiZhang/StarGAN_Norland
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
https://github.com/SummerHuiZhang/StarGAN_test
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
https://github.com/Superminionsfy/Personalized-Data-Generation-using-GAN
CollaGAN : Collaborative GAN for Missing Image Data Imputation
https://github.com/Surojit-KB/ARTY
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/T-C-J/GAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/T-C-J/shuiying
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/T9-LIN/MSc-Project
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/TAMU-VITA/GAN-Slimming
AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks
https://github.com/THU-DA-6D-Pose-Group/GDR-Net
SO-Pose: Exploiting Self-Occlusion for Direct 6D Pose Estimation
https://github.com/THUDM/CodeGeeX
CodeBLEU: a Method for Automatic Evaluation of Code Synthesis
https://github.com/THUDM/KOBE
https://github.com/THUNLP-MT/Document-Transformer
Improving the Transformer Translation Model with Document-Level Context
https://github.com/THUcqb/KOBE
https://github.com/TIANHAO-WANG/nnstack
https://github.com/TJJTJJTJJ/pytorch_CycleGAN_frame
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/TSLNIHAOGIT/QANet_keras_debug
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
https://github.com/TSLNIHAOGIT/bert
https://github.com/TSLNIHAOGIT/bert_run
https://github.com/TYTYTYTYTYTYTYTYTY/558-project
https://github.com/TaiseiAso/BiGruAttEncDec
A Simple, Fast Diverse Decoding Algorithm for Neural Generation
https://github.com/TangDL/DCN
R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/TanyaChutani/Pix2pix-Tf2.x
Image-to-Image Translation with Conditional Adversarial Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.