Papers with Code 아카이브

NLP based Person Retrival

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코드

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

https://github.com/KrishnaDN/E2E_ASR_Confidence_Estimation

Confidence Estimation for Attention-based Sequence-to-sequence Models for Speech Recognition

https://github.com/KrishnaTarun/Deep-Learning-Lab

Attention Is All You Need

https://github.com/Kumar-Tarun/summarization

A Deep Reinforced Model for Abstractive Summarization

https://github.com/KunZhou9646/controllable_evc_code

Seen and Unseen emotional style transfer for voice conversion with a new emotional speech dataset

https://github.com/Kunhao-Liu/StyleGaussian

StyleGaussian: Instant 3D Style Transfer with Gaussian Splatting

https://github.com/KushGrandhi/Polaroid

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

https://github.com/Kyubyong/transformer

Attention Is All You Need

https://github.com/L-A-Sandhu/Swin-Unet-

Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation

https://github.com/LAION-AI/phenaki

MaskGIT: Masked Generative Image Transformer

https://github.com/LKLQQ/MIMO-UNet

Rethinking Coarse-to-Fine Approach in Single Image Deblurring

https://github.com/LKLQQ/detr

End-to-End Object Detection with Transformers

https://github.com/LONG-9621/IQA03

MS-UNIQUE: Multi-model and Sharpness-weighted Unsupervised Image Quality Estimation

https://github.com/LONG-9621/SketchTo3D

3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks

https://github.com/LastReLU/Reflection-Separation

Single Image Reflection Removal Using Deep Encoder-Decoder Network

https://github.com/LastReLU/ReflectionSeparation

Single Image Reflection Removal Using Deep Encoder-Decoder Network

https://github.com/Latterlig96/DCUnet

DC-UNet: Rethinking the U-Net Architecture with Dual Channel Efficient CNN for Medical Images Segmentation

https://github.com/Lausannen/NAS-FCOS

NAS-FCOS: Fast Neural Architecture Search for Object Detection

https://github.com/LawrenceDuan/myLASER

Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond

https://github.com/LeeDongYeun/keras-m2det

M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network

https://github.com/LeenaShekhar/NLP-Linguistics-ML-Resources

Recurrent Neural Networks in Linguistic Theory: Revisiting Pinker and Prince (1988) and the Past Tense Debate

https://github.com/LeenaShekhar/NLP-ML-Resources

Recurrent Neural Networks in Linguistic Theory: Revisiting Pinker and Prince (1988) and the Past Tense Debate

https://github.com/Lemoners/Attention-Is-All-You-Need

Attention Is All You Need

https://github.com/LeoWood/bert-horovod

Attention Is All You Need

https://github.com/Leonardo-Blanger/detr_tensorflow

End-to-End Object Detection with Transformers

https://github.com/LeonardoBerti07/Multi-Horizon-Forecasting-for-Limit-Order-Books

Multi-Horizon Forecasting for Limit Order Books: Novel Deep Learning Approaches and Hardware Acceleration using Intelligent Processing Units

https://github.com/Li-Chongyi/Ucolor

Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding

https://github.com/Li-ai-cell/Interpretation_DETR

End-to-End Object Detection with Transformers

https://github.com/LiamMaclean216/Pytorch-Chatbot

Attention Is All You Need

https://github.com/LiamMaclean216/Pytorch-Transfomer

Attention Is All You Need

https://github.com/Lieberk/Paddle-AoA-Captioning

Attention on Attention for Image Captioning

https://github.com/LinXueyuanStdio/LaTeX_OCR_PRO

Image-to-Markup Generation with Coarse-to-Fine Attention

https://github.com/Line290/EDD-third-party

Image-based table recognition: data, model, and evaluation

https://github.com/Lireanstar/BPDG

A Pre-training Based Personalized Dialogue Generation Model with Persona-sparse Data

https://github.com/Liut2016/stackedResBiLSTM

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

https://github.com/LiyuanLucasLiu/Transforemr-Clinic

Very Deep Transformers for Neural Machine Translation

https://github.com/LiyuanLucasLiu/Transformer-Clinic

Very Deep Transformers for Neural Machine Translation

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