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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/FingerRec/DSM

Spatial Transformer Networks

https://github.com/Fireblossom/DeepDarkHomeword

Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling

https://github.com/Fireblossom/DeepDarkHomework

Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling

https://github.com/FiveEyes/CycleGAN

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

https://github.com/Flajt/Veritas

Neural Machine Translating from Natural Language to SPARQL

https://github.com/Flawless1202/Transformer

Weighted Transformer Network for Machine Translation

https://github.com/FlorianPfisterer/2D-LSTM-Seq2Seq

Attention Is All You Need

https://github.com/FrancescoMarchesini/pixToPix

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/Francois-Aubet/EQuANt

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

https://github.com/FreddeFrallan/Multilingual-CLIP

Language-agnostic BERT Sentence Embedding

https://github.com/FredericGodin/QuasiRNN-DReLU

Recurrent Neural Network Regularization

https://github.com/Fuchai/language

Attention Is All You Need

https://github.com/Fuhongshuai/FUHS_GraphCNN

Spectral Networks and Locally Connected Networks on Graphs

https://github.com/Fuhui-Chen/TecoGAN

Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation

https://github.com/Funaizhang/dics

Improving Shape Deformation in Unsupervised Image-to-Image Translation

https://github.com/G-4-R-Y/Tweet-Sentiment-Extraction

Attention Is All You Need

https://github.com/G-4-R-Y/Tweet-Sentiment-Extraction-roBERTa-5fold

Attention Is All You Need

https://github.com/GBATZOLIS/Wespe-Keras

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

https://github.com/GH3927/CycleGAN-applied-to-cranes

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

https://github.com/GH3927/Pix2Pix-applied-to-cranes

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/GH3927/pix2pix_linemod_duck

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/GJTNB/reading-memo

Structured Domain Adaptation with Online Relation Regularization for Unsupervised Person Re-ID

https://github.com/GT-SALT/AAAI_CLF

Semi-Supervised Models via Data Augmentationfor Classifying Interactive Affective Responses

https://github.com/Gaozhen0816/BERT_QA_For_AILaw

Attention Is All You Need

https://github.com/Gaozhen0816/BERT_QA_for_Chinese

Attention Is All You Need

https://github.com/Gintasp/xgan

XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings

https://github.com/GitHubXlong/TecoGAN

Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation

https://github.com/GiuliaLanzillotta/exercises

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

https://github.com/GlassyWing/transformer-keras

Attention Is All You Need

https://github.com/Gldkslfmsd/t2t_second

Morphological and Language-Agnostic Word Segmentation for NMT

https://github.com/Goodideax/lstm-negtive

Recurrent Neural Network Regularization

https://github.com/Goodideax/rnn_neg_efficient

Recurrent Neural Network Regularization

https://github.com/Gowthamkar6/Image-captioning-using-CNN-RNN

Show and Tell: A Neural Image Caption Generator

https://github.com/Guillem96/pointer-nn-pytorch

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/HA-Transformer/HA-Transformer

Multi-branch Attentive Transformer

https://github.com/HB-Liu/Improved-SemHash-and-Gumbel-Softmax

Discrete Autoencoders for Sequence Models

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