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Visual Storytelling
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
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<span style="color:grey; opacity: 0.6">( Image credit: No Metrics Are Perfect )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Kirankumaraswamy/Transformers
https://github.com/KrishnaTarun/Deep-Learning-Lab
https://github.com/Kyubyong/transformer
https://github.com/Lemoners/Attention-Is-All-You-Need
https://github.com/LeoWood/bert-horovod
https://github.com/LiamMaclean216/Pytorch-Chatbot
https://github.com/LiamMaclean216/Pytorch-Transfomer
https://github.com/LivXue/MMT
https://github.com/LoveYang/bert_test
https://github.com/Lsdefine/attention-is-all-you-need-keras
https://github.com/MCLYang/RhythmGAN_pytorch
https://github.com/MOHAMEDELDAKDOUKY/bert_adjusted
https://github.com/MaZhiyuanBUAA/bert-tf1.4.0
https://github.com/Maple728/transformer
https://github.com/MarcBS/keras
https://github.com/MashaKubyshina/learning_ML
https://github.com/Matthewdowney18/Transformer_Dialogue
https://github.com/Maz101/Bert
https://github.com/Maz101/pretrainingdataset
https://github.com/MichaelZhouwang/LMlexsub
https://github.com/MindCode-4/code-5/tree/main/transformer
https://github.com/MindSpore-scientific/code-11/tree/main/Translation-Invariant/model
https://github.com/MindSpore-scientific/code-8/tree/main/Translation-Invariant/model
https://github.com/MirunaPislar/multi-head-attention-labeller
https://github.com/Misoknisky/Bert-MultiGpu
https://github.com/MohamedAbdelsalam9/TT-Transformer
https://github.com/NVIDIA/sentiment-discovery
https://github.com/Neoanarika/torchexplainer
https://github.com/Nimesh-Patel/BERT
https://github.com/Nstats/bert_MRC
https://github.com/Nstats/bert_senti_analysis_ch
https://github.com/Nstats/my_bert
https://github.com/OPAYA/Transformer
https://github.com/Osobarako/alduswarrensewell
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/machine_translation/transformer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.