Papers with Code 아카이브

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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

0

평가표

4

보존된 평가 기록

5

코드

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

https://github.com/pwc-1/Paper-5/tree/main/mt5

mT5: A massively multilingual pre-trained text-to-text transformer

https://github.com/pwc-1/Paper-9/tree/main/2/mbart

Multilingual Denoising Pre-training for Neural Machine Translation

https://github.com/pwc-1/Paper-9/tree/main/2/mbart50

IndicBART: A Pre-trained Model for Indic Natural Language Generation

https://github.com/pwc-1/Paper-9/tree/main/2/mt5

MT6: Multilingual Pretrained Text-to-Text Transformer with Translation Pairs

https://github.com/pwc-1/Paper-9/tree/main/4/Translation-Invariant/model

Attention Is All You Need

https://github.com/pwc-1/Paper-9/tree/main/7/Translation-Invariant/model

On Translation Invariance in CNNs: Convolutional Layers can Exploit Absolute Spatial Location

https://github.com/pwc-1/Paper-9/tree/main/marian

Marian: Fast Neural Machine Translation in C++

https://github.com/pytorch/fairseq/tree/master/examples/wmt19

Facebook FAIR's WMT19 News Translation Task Submission

https://github.com/pytorch/pippy

GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

https://github.com/pytorch/tau

GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

https://github.com/qcri/dialectal_arabic_segmenter

Arabic Multi-Dialect Segmentation: bi-LSTM-CRF vs. SVM

https://github.com/qcri/dialectal_arabic_tools

Arabic Multi-Dialect Segmentation: bi-LSTM-CRF vs. SVM

https://github.com/qfzhu/st

Style Transfer from Non-Parallel Text by Cross-Alignment

https://github.com/qibinc/KOBE

BERTScore: Evaluating Text Generation with BERT

https://github.com/qijimrc/tensor2tensor

Tensor2Tensor for Neural Machine Translation

https://github.com/qilei123/DEEPLAB_4_RETINA

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

https://github.com/qilei123/DEEPLAB_4_RETINAIMG

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

https://github.com/qilei123/DeformableConvV2

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

https://github.com/qilei123/DeformableConvV2_crop

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

https://github.com/qilei123/fpn_crop

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

https://github.com/qilei123/fpn_crop_v1_5d

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

https://github.com/qilei123/sod_v1

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

https://github.com/qilei123/sod_v1_demo

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

https://github.com/qingzew/tensorflow-ram

Recurrent Models of Visual Attention

https://github.com/qiuguoxia/chatbotmodal

Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets

https://github.com/qq345736500/sarcasm

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/qu-arx/arx-inf

Recent Advances in Neural Program Synthesis

https://github.com/quatrope/ois

A New Algorithm For Difference Image Analysis

https://github.com/qubvel/efficientnet

GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

https://github.com/quickjkee/eff-diff-edit

Navigating the GAN Parameter Space for Semantic Image Editing

https://github.com/qywu/memformers

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

https://github.com/r00tsyst3m/snp

Stylized Neural Painting

https://github.com/r06922019/butt_lion_paper_notes

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/ra1nty/sru-naive

Simple Recurrent Units for Highly Parallelizable Recurrence

https://github.com/rachtibat/lrp-explains-transformers

Quantifying the Plausibility of Context Reliance in Neural Machine Translation

https://github.com/rachtibat/lrp-for-transformers

Quantifying the Plausibility of Context Reliance in Neural Machine Translation

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