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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/ShiYaya/Awesome_Evaluation_Metrics_for_Text_Generation
https://github.com/ShichengChen/WaveNetSeparateAudio
https://github.com/Shilin-LU/TF-ICON
Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation
https://github.com/Shinya-Kouda/kgc
https://github.com/ShivamGaurUQ/Automated-hashtag-generation-using-Deep-Learning
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/ShivamRajSharma/Transformer-Architectures-From-Scratch
https://github.com/Shivendra-psc/speechbot
https://github.com/Shivmohith/Visual-Assistance-for-the-Blind
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/ShotDownDiane/tcn-master
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
https://github.com/ShroukAbozeid/ImageCaptionByVoice
Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge
https://github.com/Shubham-SK/TreeOverAte
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/Shubham-SK/kronos
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/Shumway82/CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/ShuvenduBikash/transformer_spelling_corrector
https://github.com/ShwetaBaranwal/Transformer
https://github.com/Siddharth1698/Image-Captioning-with-Deep-Learning-and-NLP
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/Siddharth1698/Image-Captioning-with-Inception-LSTM
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/Siddhartha24795/Medical-Image-Synthesis
https://github.com/SimengSun/CIS530-project
SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation
https://github.com/SimeonKraev/Musical-style-transfer-with-cycle-consistent-generative-adversarial-neural-networks
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Simon4Yan/Learning-via-Translation
https://github.com/SimonDele/Glossary
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/SimulTrans-demo/STACL
https://github.com/SkafteNicki/ddtn
https://github.com/Skumarr53/Attention-is-All-you-Need-PyTorch
https://github.com/Skumarr53/Image-Caption-Generation-using-Fastai
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
https://github.com/Smerity/keras_snli
https://github.com/Smilels/multimodal-translation-teleop
A Mobile Robot Hand-Arm Teleoperation System by Vision and IMU
https://github.com/Somefive/XNLI
https://github.com/Songweiping/TCN-TF
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
https://github.com/Sontref/CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/SoonminHwang/rgbt-ped-detection
https://github.com/Sooram/cnn
https://github.com/Sooram/stn
https://github.com/SophiaYuSophiaYu/ImageCaption
Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.