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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/shantipriyap/odia-nlp-resource-catalog

PMIndia -- A Collection of Parallel Corpora of Languages of India

https://github.com/shao-chi/ImageCaption

Attention Is All You Need

https://github.com/shaoanlu/fewshot-face-translation-GAN

Few-Shot Unsupervised Image-to-Image Translation

https://github.com/shaominghe/stargan_adience

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

https://github.com/shaox192/NeuralGuidance

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

https://github.com/shashanksiripragada/pib-crawl

A Multilingual Parallel Corpora Collection Effort for Indian Languages

https://github.com/shashiongithub/XSum

Convolutional Sequence to Sequence Learning

https://github.com/shawnkx/Fast-MoS

Fast and Simple Mixture of Softmaxes with BPE and Hybrid-LightRNN for Language Generation

https://github.com/shawnkx/fully-nat

Fully Non-autoregressive Neural Machine Translation: Tricks of the Trade

https://github.com/shawntan/rtdp

Recursive Top-Down Production for Sentence Generation with Latent Trees

https://github.com/shawnyxiao/textclassification-keras

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/sherinaseri/bert

Attention Is All You Need

https://github.com/shibing624/pycorrector

Neural Language Correction with Character-Based Attention

https://github.com/shijianjian/efficientnet-pytorch-3d

GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

https://github.com/shijx12/kqapro_baselines

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

https://github.com/shikhadahiya/Image-to-image-translation-using-C-GAN

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/shikhar1sharma/NLP-Resources

QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension

https://github.com/shinde-Pratik/Image-Restoration

Bringing Old Photos Back to Life

https://github.com/shirley-wu/simul-mt_temporally-correlated-task-scheduling

Temporally Correlated Task Scheduling for Sequence Learning

https://github.com/shishir-roy/decaNLP-with-BERT

The Natural Language Decathlon: Multitask Learning as Question Answering

https://github.com/shivam13juna/Sequence_Prediction_LSTM_CHAR

Recurrent Neural Network Regularization

https://github.com/shivammehta25/Matcha-TTS

Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

https://github.com/shivani-raul/Image-Captioning-VGG16

Image Captioning using Deep Neural Architectures

https://github.com/shivendrashahi/Final_Project_Traffic_Sign

Traffic Sign Classification Using Deep Inception Based Convolutional Networks

https://github.com/shlokmehrotra/Convocare

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/shmsw25/bart-closed-book-qa

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

https://github.com/shouvikcirca/Transformer

Attention Is All You Need

https://github.com/shouvikcirca/Transformer_MiniProject

Attention Is All You Need

https://github.com/shreshtashetty/CycleGANPhototoMonet

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

https://github.com/shreyash8/English_to_Hindi_NMT

Attention Is All You Need

https://github.com/shreyashankar/gpt3-sandbox

Attention Is All You Need

https://github.com/shridhivyah/starGAN

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

https://github.com/shuaishuaij/Machine-Translation

Attention Is All You Need

https://github.com/shubhtuls/stn3d

Spatial Transformer Networks

https://github.com/shuhuayu/dist-sign-momentum

SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum

https://github.com/shun601/4th-tellus-satellite-challenge

On the Variance of the Adaptive Learning Rate and Beyond

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