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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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

https://github.com/christianspybrook/article_recommender

Attention Is All You Need

https://github.com/chriswegmann/drone_steering

Beyond Temporal Pooling: Recurrence and Temporal Convolutions for Gesture Recognition in Video

https://github.com/chunghyunhee/twitter_disaster_NLP

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/chunhuililili/mt_dnn

On the Variance of the Adaptive Learning Rate and Beyond

https://github.com/chunlei2/show-and-tell

Show and Tell: A Neural Image Caption Generator

https://github.com/cianfrocco-lab/GAN-for-Cryo-EM-image-denoising

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/cifkam/focalposepp

Focal Length and Object Pose Estimation via Render and Compare

https://github.com/cindyxinyiwang/DataSelection

Balancing Training for Multilingual Neural Machine Translation

https://github.com/cindyxinyiwang/TextStyleTransfer

A Probabilistic Formulation of Unsupervised Text Style Transfer

https://github.com/cisnlp/simalign

SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings

https://github.com/cjf8899/ReMix_Pytorch

ReMix: Towards Image-to-Image Translation with Limited Data

https://github.com/clementchadebec/benchmark_VAE

Autoencoding beyond pixels using a learned similarity metric

https://github.com/cleye/FUNIT-Fringe

Few-Shot Unsupervised Image-to-Image Translation

https://github.com/clmbrs/communication-translation

Multilingual Denoising Pre-training for Neural Machine Translation

https://github.com/clock-me/text-restyle

Multiple-Attribute Text Style Transfer

https://github.com/cloneofsimo/RealFormer-pytorch

RealFormer: Transformer Likes Residual Attention

https://github.com/cloudmcloudyo/capstone

Attention Is All You Need

https://github.com/cloudwaysx/cyclegan-tensorflow_pixie

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

https://github.com/cmsflash/efficient-attention

Attention Is All You Need

https://github.com/coco60/bert-test

Attention Is All You Need

https://github.com/code-desc/codesc

Unsupervised Translation of Programming Languages

https://github.com/code-implementation1/Code1/tree/main/dgcnet_res101

DGC-Net: Dense Geometric Correspondence Network

https://github.com/code-implementation1/Code1/tree/main/dnet_nas

D-Net: Siamese based Network with Mutual Attention for Volume Alignment

https://github.com/code-implementation1/Code6/tree/main/Pix2Pix

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/code-implementation1/Code7/tree/main/rfcn

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

https://github.com/codefuse-ai/codefuse-evaluation

CodeBLEU: a Method for Automatic Evaluation of Code Synthesis

https://github.com/codehacpj/Image_description

Show and Tell: A Neural Image Caption Generator

https://github.com/codekansas/keras-language-modeling

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

https://github.com/cohere-labs-community/m-rewardbench

M-RewardBench: Evaluating Reward Models in Multilingual Settings

https://github.com/colaprograms/speechify

Sequence-to-Sequence Models Can Directly Translate Foreign Speech

https://github.com/coldmanck/show-attend-and-tell

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

https://github.com/colorfulscoop/tfdlg

Attention Is All You Need

https://github.com/componavt/wikokit

Multilingual ontology matching based on Wiktionary data accessible via SPARQL endpoint

https://github.com/conan7882/recurrent-attention-model

Recurrent Models of Visual Attention

https://github.com/conceptofmind/LaMDA-pytorch

Attention Is All You Need

https://github.com/conceptofmind/lamda-rlhf-pytorch

Attention Is All You Need

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