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코드

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

https://github.com/longbowking/bert

Attention Is All You Need

https://github.com/longjj/Caffe-SGDR

Show and Tell: A Neural Image Caption Generator

https://github.com/lornatang/cyclegan-pytorch

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

https://github.com/lottens/QA-SQuAD

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

https://github.com/lovecambi/qebrain

"Bilingual Expert" Can Find Translation Errors

https://github.com/lovedavidsilva/bert_old_version

Attention Is All You Need

https://github.com/lovit/KoBERTScore

BERTScore: Evaluating Text Generation with BERT

https://github.com/lovit/ko-BERTScore

BERTScore: Evaluating Text Generation with BERT

https://github.com/lql-team/TSDM

SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks

https://github.com/lszhuhaichao/Deep-Video-Deinterlacing

Real-time Deep Video Deinterlacing

https://github.com/luca310795/tensor-field-networks

Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds

https://github.com/lucasbotang/modulargan

Modular Generative Adversarial Networks

https://github.com/lucasfporto/yolactTest

YOLACT: Real-time Instance Segmentation

https://github.com/lucidrains/FLASH-pytorch

Sparse Attention with Linear Units

https://github.com/lucidrains/RETRO-pytorch

DeepNet: Scaling Transformers to 1,000 Layers

https://github.com/lucidrains/feedback-transformer-pytorch

Addressing Some Limitations of Transformers with Feedback Memory

https://github.com/lucidrains/marge-pytorch

Pre-training via Paraphrasing

https://github.com/lucidrains/mixture-of-experts

GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

https://github.com/lucidrains/omninet-pytorch

OmniNet: Omnidirectional Representations from Transformers

https://github.com/lucidrains/res-mlp-pytorch

ResMLP: Feedforward networks for image classification with data-efficient training

https://github.com/lucidrains/se3-transformer-pytorch

SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks

https://github.com/lucidrains/vector-quantize-pytorch

Exploration into Translation-Equivariant Image Quantization

https://github.com/lucidrains/x-transformers

Augmenting Self-attention with Persistent Memory

https://github.com/luckynozomi/PPI_Bert

Attention Is All You Need

https://github.com/lucylow/En_francais_si_vous_plait-

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/lucylow/Yeezy-Taught-Me

Grid Long Short-Term Memory

https://github.com/lukasuz/Banding-Pattern-Extraction

Deep Anomaly Generation: An Image Translation Approach of Synthesizing Abnormal Banded Chromosome Images

https://github.com/lukeandshuo/IR2VI_journal

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/lukemelas/PyTorch-Pretrained-ViT

Attention Is All You Need

https://github.com/luofuli/DualLanST

Phrase-Based & Neural Unsupervised Machine Translation

https://github.com/luofuli/DualRL

Phrase-Based & Neural Unsupervised Machine Translation

https://github.com/luost26/CGCF-ConfGen

Learning Gradient Fields for Molecular Conformation Generation

https://github.com/luxxxlucy/recurrent-attention-model-revisited

Recurrent Models of Visual Attention

https://github.com/luyige/test-git

Massive Exploration of Neural Machine Translation Architectures

https://github.com/lvyufeng/image_captioning_mindspore

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

https://github.com/lvyufeng/mindspore-nlp-tutorial

Attention Is All You Need

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