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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

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평가표

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보존된 평가 기록

5

코드

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

https://github.com/kekeliu-whu/MSF_LOAM

A Tightly Coupled LiDAR-IMU Odometry through Iterated Point-Level Undistortion

https://github.com/kekliu/MSF_LOAM

A Tightly Coupled LiDAR-IMU Odometry through Iterated Point-Level Undistortion

https://github.com/kelly2016/multi-label-bert

Attention Is All You Need

https://github.com/kelvinxu/arctic-captions

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

https://github.com/ken-power/SensorFusionND-3D-Object-Tracking

Improvements to Target-Based 3D LiDAR to Camera Calibration

https://github.com/kenkai21/Image_Captioning

Show and Tell: A Neural Image Caption Generator

https://github.com/kentchang/ca_divergence

Lévy Flights of the Collective Imagination

https://github.com/kenya-sk/show_attend_and_tell

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

https://github.com/keonlee9420/Parallel-Tacotron2

Attention Is All You Need

https://github.com/keonlee9420/Stepwise_Monotonic_Multihead_Attention

Online and Linear-Time Attention by Enforcing Monotonic Alignments

https://github.com/keonlee9420/VAENAR-TTS

FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow

https://github.com/keshavganapathy/genetic-algorithm-jhu

A Study of Genetic Algorithms for Hyperparameter Optimization of Neural Networks in Machine Translation

https://github.com/kevinduh/san_mrc

Multi-task Learning with Sample Re-weighting for Machine Reading Comprehension

https://github.com/kevinlu1248/pyate

ATR4S: Toolkit with State-of-the-art Automatic Terms Recognition Methods in Scala

https://github.com/kevinzakka/recurrent-visual-attention

Recurrent Models of Visual Attention

https://github.com/kevinzakka/spatial-transformer-network

Spatial Transformer Networks

https://github.com/keyonvafa/career-code

Rationales for Sequential Predictions

https://github.com/kh-kim/simple-nmt

Effective Approaches to Attention-based Neural Machine Translation

https://github.com/kh-mo/QA_wikisql

Neural Machine Translation of Rare Words with Subword Units

https://github.com/khangt1k25/Image-Captioning

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

https://github.com/kheeong/XLM_OWN

Phrase-Based & Neural Unsupervised Machine Translation

https://github.com/khush3/GAN

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

https://github.com/khushsi/Finetuning_BART_for_FACET_Summarization

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

https://github.com/khwajawisal/Attention-augmented-Convolutional-Neural-Networks

Attention Is All You Need

https://github.com/kiko441500/google_bert

Attention Is All You Need

https://github.com/kingcheng2000/GAN

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/kingcheng2000/bert

Attention Is All You Need

https://github.com/kingcong/TCN

An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

https://github.com/kingcong/gpu_gru

Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

https://github.com/kingsaint/Wikidata-Descriptions

Relevance of Unsupervised Metrics in Task-Oriented Dialogue for Evaluating Natural Language Generation

https://github.com/kingstarcraft/speech-to-text-wavenet2

Neural Machine Translation in Linear Time

https://github.com/kinik93/Spatial-Transformer-Network-application

Spatial Transformer Networks

https://github.com/kinimod23/ATS_Project

Neural Machine Translation in Linear Time

https://github.com/kirbiyik/caption-it

Show and Tell: A Neural Image Caption Generator

https://github.com/kiwi1944/CRISense

Recurrent Models of Visual Attention

https://github.com/kiyohiro8/pytorch-unpaired-image-to-image-translation

Attention-Guided Generative Adversarial Networks for Unsupervised Image-to-Image Translation

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