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

https://github.com/sss20young/Chuibbo-Flask-Server

StarGAN v2: Diverse Image Synthesis for Multiple Domains

https://github.com/stair-lab/villm-eval

BERTScore: Evaluating Text Generation with BERT

https://github.com/stanford-oval/genienlp

The Natural Language Decathlon: Multitask Learning as Question Answering

https://github.com/stanfordmlgroup/nlc

Neural Language Correction with Character-Based Attention

https://github.com/stanfordmlgroup/nlm-noising

Data Noising as Smoothing in Neural Network Language Models

https://github.com/starry91/NMT-Lab

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

https://github.com/stefan-it/deep-eos

Fully Character-Level Neural Machine Translation without Explicit Segmentation

https://github.com/stefan-it/fine-tuned-berts-seq

How multilingual is Multilingual BERT?

https://github.com/stefannatu/NLP-Translation-Models

Convolutional Sequence to Sequence Learning

https://github.com/stefanos50/CARLA2Real

Enhancing Photorealism Enhancement

https://github.com/steinbeck/DeepOCSR

End-to-End Attention-based Image Captioning

https://github.com/stephanboner/gabor_data

Recurrent Models of Visual Attention

https://github.com/stevebong31/stargan

StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

https://github.com/steventan0110/star

Nugget: Neural Agglomerative Embeddings of Text

https://github.com/stevinc/Transformer_Timeseries

Attention Is All You Need

https://github.com/stingram/CV_Image_Captioning

Show and Tell: A Neural Image Caption Generator

https://github.com/stoensin/IC

Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge

https://github.com/stqc/Simple_CycleGAN_pytorch

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

https://github.com/stuartchen1949/yolact-paddle

YOLACT: Real-time Instance Segmentation

https://github.com/studian/CVND_P2_Image_Captioning

Show and Tell: A Neural Image Caption Generator

https://github.com/studio-ousia/luke

Attention Is All You Need

https://github.com/stupidZZ/pyc_repo

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

https://github.com/sucheta19/Text-Classification-Using-CNN

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

https://github.com/sumfish/music-style-transfer

Few-Shot Unsupervised Image-to-Image Translation

https://github.com/sumith1896/spoc

SPoC: Search-based Pseudocode to Code

https://github.com/summerice9/AniGAN

AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation

https://github.com/sunnysinghnitb/text-corrector-software

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/sunnysinghnitb/text_corrector_software

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/sunset1995/pytorch-layoutnet

LayoutNet: Reconstructing the 3D Room Layout from a Single RGB Image

https://github.com/superli3/CYRMPR

code2seq: Generating Sequences from Structured Representations of Code

https://github.com/superli3/codenavi

code2seq: Generating Sequences from Structured Representations of Code

https://github.com/supreethub/Image-Captioning

Show and Tell: A Neural Image Caption Generator

https://github.com/surafelml/improving-zeroshot-nmt

Improving Zero-Shot Translation of Low-Resource Languages

https://github.com/suryachintu/Quora-Insincere-Questions-Kaggle

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/sustcsonglin/flash-linear-attention

Linear Transformers Are Secretly Fast Weight Programmers

https://github.com/suzhiba/SimpleRecurrentUnits-SRU-

Simple Recurrent Units for Highly Parallelizable Recurrence

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