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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/floydhub/word-language-model

Recurrent Neural Network Regularization

https://github.com/flxst/nerblackbox

Attention Is All You Need

https://github.com/flyliu2017/bert

Attention Is All You Need

https://github.com/flyliu2017/bert_modularized

Attention Is All You Need

https://github.com/fourmi1995/IronExperiment-DCN

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

https://github.com/fqyshelly/CS224n-Winter2019-assignment-solutions

Effective Approaches to Attention-based Neural Machine Translation

https://github.com/fra-mari/The-Karl-Marx-Press-Review

Attention Is All You Need

https://github.com/francescodisalvo05/66DaysOfData

Quasi-Recurrent Neural Networks

https://github.com/frankcgq105/BERTCHEN

Attention Is All You Need

https://github.com/franknb/Self-attention-DCGAN

Attention Is All You Need

https://github.com/franknb/Text-Summarization

Attention Is All You Need

https://github.com/fraxea/pix2pix

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/freedombenLiu/MUSE

Word Translation Without Parallel Data

https://github.com/freedombenLiu/speech-to-text-wavenet

Neural Machine Translation in Linear Time

https://github.com/freeflyxiaoma/pycorrector

Neural Language Correction with Character-Based Attention

https://github.com/freeniliang/caffe-fastercnn-rfcn

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

https://github.com/freeniliang/caffe-ssd

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

https://github.com/freesunshine0316/sembleu

Neural Semantic Parsing by Character-based Translation: Experiments with Abstract Meaning Representations

https://github.com/frgfm/Holocron

On the Variance of the Adaptive Learning Rate and Beyond

https://github.com/frgsimpson/kitt

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

https://github.com/frozentoad9/Neural-Machine-Translation

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/fshdnc/enfi-XLM

Phrase-Based & Neural Unsupervised Machine Translation

https://github.com/fstahlberg/tensor2tensor

Attention Is All You Need

https://github.com/fstahlberg/tensor2tensor-usr

Deep Recurrent Models with Fast-Forward Connections for Neural Machine Translation

https://github.com/ftramer/Handcrafted-DP

Deep Roto-Translation Scattering for Object Classification

https://github.com/fuenwang/CVFX-hw1

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

https://github.com/fuqianya/show-attend-and-tell-paddle

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

https://github.com/fuqichen1998/eecs442-final-project-show-and-tell

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

https://github.com/fuqinshen/UGATIT-pytorch

U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation

https://github.com/fushengwuyu/R-Drop

R-Drop: Regularized Dropout for Neural Networks

https://github.com/fwbrandao/Abstractive_Summarisation

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

https://github.com/g-u-n/pycil

FeTrIL: Feature Translation for Exemplar-Free Class-Incremental Learning

https://github.com/g1910/HyperNetworks

HyperNetworks

https://github.com/g61412333/pix2pix-tensorflow

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/gabinsane/multi-vaes-in-robotics

Multimodal Generative Models for Scalable Weakly-Supervised Learning

https://github.com/gabinsane/multimodal-vae-comparison

Multimodal Generative Models for Scalable Weakly-Supervised Learning

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