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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/jazzsaxmafia/show_and_tell.tensorflow

Show and Tell: A Neural Image Caption Generator

https://github.com/jb33k/awd-lstm-lm-ThinkNet

Regularizing and Optimizing LSTM Language Models

https://github.com/jbdel/OMG_UMONS_submission

Attention Is All You Need

https://github.com/jcchiba/kamonData

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

https://github.com/jcchiba/sanseriffontdatasetandmodel

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

https://github.com/jchenghu/sacreeos

A Call for Clarity in Reporting BLEU Scores

https://github.com/jean-kunz/ml_research_papers

Attention Is All You Need

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

Show and Tell: A Neural Image Caption Generator

https://github.com/jelifysh/Transformers

Attention Is All You Need

https://github.com/jeongchanyoung-1234/De-En-NMT

Attention Is All You Need

https://github.com/jeongukjae/smaller-labse

Language-agnostic BERT Sentence Embedding

https://github.com/jerinphilip/fairseq-ilmt

A Multilingual Parallel Corpora Collection Effort for Indian Languages

https://github.com/jerryyli/valhalla-nmt

VALHALLA: Visual Hallucination for Machine Translation

https://github.com/jessefokkinga/MechanismsOfAction

When Does Label Smoothing Help?

https://github.com/jewettaij/superpose3d

TEASER: Fast and Certifiable Point Cloud Registration

https://github.com/jgcbrouns/Introducing-Sparsity-in-the-Transformer

Attention Is All You Need

https://github.com/jhave/RERITES-AvgWeightDescentLSTM-PoetryGeneration

Regularizing and Optimizing LSTM Language Models

https://github.com/jiacheng-xu/text-sum-uncertainty

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

https://github.com/jiahuei/Self-Critical-SCST-TensorFlow

Attention Is All You Need

https://github.com/jiajunhua/CycleGAN

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

https://github.com/jiajunhua/dbolya-yolact

YOLACT: Real-time Instance Segmentation

https://github.com/jiajunhua/facebookresearch-Detectron

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

https://github.com/jiajunhua/facebookresearch-MUSE

Exploiting Similarities among Languages for Machine Translation

https://github.com/jiali-ms/JLM

Strategies for Training Large Vocabulary Neural Language Models

https://github.com/jiangnanhugo/seq2seq_cuda

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/jianhanlim/ipr-imagecaptioning

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

https://github.com/jianqingzheng/path_planning_for_FEVAR

Abdominal Aortic Aneurysm Segmentation with a Small Number of Training Subjects

https://github.com/jiaruncao/BioCopyMechanism

MASS: Masked Sequence to Sequence Pre-training for Language Generation

https://github.com/jiequanz/Mobius_Demo

Data augmentation with Mobius transformations

https://github.com/jincan333/lot

Recurrent Neural Network Regularization

https://github.com/jinjiren/recurrent-image-annotator-web-demo

Annotation Order Matters: Recurrent Image Annotator for Arbitrary Length Image Tagging

https://github.com/jinliangwei/tensor2tensor-1.11

Tensor2Tensor for Neural Machine Translation

https://github.com/jinzhenfan/BERT

Attention Is All You Need

https://github.com/jis478/AniGAN

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

https://github.com/jishavm/TextStyleTransfer

Style Transfer from Non-Parallel Text by Cross-Alignment

https://github.com/jishubasak/Punny-Caption--Exploring-Image-Captioning

Show and Tell: A Neural Image Caption Generator

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