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

https://github.com/rosettacommons/rosettafold

SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks

https://github.com/rosinality/melgan-pytorch

MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis

https://github.com/rparkin1/intent_LSTM

Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling

https://github.com/rpuiggari/bert2

Attention Is All You Need

https://github.com/rsummers11/CADLab/tree/master/Lung_Segmentation_XLSor

XLSor: A Robust and Accurate Lung Segmentor on Chest X-Rays Using Criss-Cross Attention and Customized Radiorealistic Abnormalities Generation

https://github.com/ruanyuhui/adn-qsdl

ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction

https://github.com/ruiliu-ai/stereogan

StereoGAN: Bridging Synthetic-to-Real Domain Gap by Joint Optimization of Domain Translation and Stereo Matching

https://github.com/ruiqi-zhong/acl2021-instance-level

Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers

https://github.com/ruiyiw/VT-summ

Variational Transformers for Diverse Response Generation

https://github.com/ruoyuxie/interpretable_dialect_classifier

FRMT: A Benchmark for Few-Shot Region-Aware Machine Translation

https://github.com/rupakdas18/Research-Project-UMD-CS-5642

Attention Is All You Need

https://github.com/rupakdas18/SemEval-2017-Task-4-A-B-C-using-BERT

Attention Is All You Need

https://github.com/rupakdas18/SuperGlue-tasks-using-BERT

Attention Is All You Need

https://github.com/russiannlp/rucola

Multilingual Translation with Extensible Multilingual Pretraining and Finetuning

https://github.com/rvandewater/yaib

Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

https://github.com/rwightman/pytorch-image-models

Lookahead Optimizer: k steps forward, 1 step back

https://github.com/rxn4chemistry/OpenNMT-py

OpenNMT: Neural Machine Translation Toolkit

https://github.com/ry85/VAE-GAN

Autoencoding beyond pixels using a learned similarity metric

https://github.com/ryanwng12/img-colorization

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/s-ankur/fairseq-gec

Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data

https://github.com/s-nlp/russe_detox_2022

Language-agnostic BERT Sentence Embedding

https://github.com/s1879281/Lexical-NMT

Improving Lexical Choice in Neural Machine Translation

https://github.com/s3prl/End-to-end-ASR-Pytorch

Attention-Based Models for Speech Recognition

https://github.com/s9891326/Fine-Tuning-BERT

Attention Is All You Need

https://github.com/sabetAI/bucc-eval

Word Translation Without Parallel Data

https://github.com/sachin19/mucoco

Controlled Text Generation as Continuous Optimization with Multiple Constraints

https://github.com/safpla/AutoLossRelease

AutoLoss: Learning Discrete Schedules for Alternate Optimization

https://github.com/sahilg06/BicycleGAN

Toward Multimodal Image-to-Image Translation

https://github.com/sainimohit23/hindi-stemmer

Development of a Hindi Lemmatizer

https://github.com/saisandeep97/Chat-botV2

Effective Approaches to Attention-based Neural Machine Translation

https://github.com/sajadalipour7/persian-grapheme-to-phoneme-with-transformer

Attention Is All You Need

https://github.com/sajidahmed12/CSE495-NLP-Assignment-Unsupervised-Neural-Machine-Translation

Word Translation Without Parallel Data

https://github.com/salesforce/GeDi

Neural Machine Translation of Rare Words with Subword Units

https://github.com/salesforce/localization-xml-mt

A High-Quality Multilingual Dataset for Structured Documentation Translation

https://github.com/salesforce/pytorch-qrnn

Quasi-Recurrent Neural Networks

https://github.com/sam2125/translatotron

Direct speech-to-speech translation with a sequence-to-sequence model

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