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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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코드

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

https://github.com/nicholasbao/nlp_job

Attention Is All You Need

https://github.com/nicolafan/image-captioning-cnn-rnn

Show and Tell: A Neural Image Caption Generator

https://github.com/nier79/Machine-translation

Neural Machine Translation and Sequence-to-sequence Models: A Tutorial

https://github.com/nietao2/DeepSpeechRecognition

Attention Is All You Need

https://github.com/nikhil-iyer-97/wide-minima-density-hypothesis

A Simple but Tough-to-Beat Data Augmentation Approach for Natural Language Understanding and Generation

https://github.com/nikhilmaram/Show_and_Tell

Show and Tell: A Neural Image Caption Generator

https://github.com/nishantcoder97/cgandemo

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/nishimehta/Image_Captioning

Show and Tell: A Neural Image Caption Generator

https://github.com/nithishkaviyan/Show-and-Tell-Neural-Network-Image-Caption-Generator-

Show and Tell: A Neural Image Caption Generator

https://github.com/nkcr/overlap-ml

Regularizing and Optimizing LSTM Language Models

https://github.com/nkjsy/Neural-Machine-Translation

Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

https://github.com/nlahlaf/Text-Style-Transfer

Style Transfer from Non-Parallel Text by Cross-Alignment

https://github.com/nlinc1905/dsilt-tsa

Attention Is All You Need

https://github.com/nlmatics/llmsherpa

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

https://github.com/nlp-detoxify/fudge-controlled-generation

FUDGE: Controlled Text Generation With Future Discriminators

https://github.com/nmfisher/bert-modified

Attention Is All You Need

https://github.com/noafish/SketchPatch

SketchPatch: Sketch Stylization via Seamless Patch-level Synthesis

https://github.com/noah-rush/Portrait-Genre-GANs

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

https://github.com/noise-field/Russian-ULMFit

Regularizing and Optimizing LSTM Language Models

https://github.com/noriyukipy/tfchat

Attention Is All You Need

https://github.com/noriyukipy/tfdlg

Attention Is All You Need

https://github.com/northeastsquare/effficientnet

GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

https://github.com/nrc-cnrc/sockeye-multisource

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/nsarang/lookahead_keras

Lookahead Optimizer: k steps forward, 1 step back

https://github.com/nsfzyzz/generalization_metrics_for_nlp

Attention Is All You Need

https://github.com/ntunlp/eval-anaphora

Evaluating Pronominal Anaphora in Machine Translation: An Evaluation Measure and a Test Suite

https://github.com/nvshrao/AlignAndTranslate

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/nvshrao/Bidirectional-Transformer

Attention Is All You Need

https://github.com/oadonca/ANVAE

Autoencoding beyond pixels using a learned similarity metric

https://github.com/oaqa/knn4qa

Exploring Classic and Neural Lexical Translation Models for Information Retrieval: Interpretability, Effectiveness, and Efficiency Benefits

https://github.com/oarriaga/neural_image_captioning

Show and Tell: A Neural Image Caption Generator

https://github.com/oarriaga/spatial_transformer_networks

Spatial Transformer Networks

https://github.com/odin07/ddsvsvsdvstest

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

https://github.com/ofirpress/sandwich_transformer

Improving Transformer Models by Reordering their Sublayers

https://github.com/okkteam/Transformer-Transducer

Attention Is All You Need

https://github.com/oliverquintana/cGAN

Image-to-Image Translation with Conditional Adversarial Networks

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