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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/CompVis/net2net
공식 코드Network-to-Network Translation with Conditional Invertible Neural Networks
https://github.com/Cosmos-Break/transfer-mt-submit
공식 코드https://github.com/CreativePapers/Learn-to-Synthesize-and-Synthesize-to-Learn
공식 코드https://github.com/CristianLazoQuispe/skin-lesion-segmentation-using-pix2pix
공식 코드Segmentation of skin lesions and their attributes using Generative Adversarial Networks
https://github.com/CyberAgentAILab/adaptive-mbr
공식 코드Hyperparameter-Free Approach for Faster Minimum Bayes Risk Decoding
https://github.com/CyndxAI/QKNorm
공식 코드https://github.com/D-Keqi/LS-Transducer-SST
공식 코드Label-Synchronous Neural Transducer for E2E Simultaneous Speech Translation
https://github.com/DC1991/FS-Net
공식 코드https://github.com/DC1991/G2L_Net
공식 코드G2L-Net: Global to Local Network for Real-time 6D Pose Estimation with Embedding Vector Features
https://github.com/DCSaunders/gender-debias
공식 코드Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problem
https://github.com/DCSaunders/tagged-gender-coref
공식 코드Neural Machine Translation Doesn't Translate Gender Coreference Right Unless You Make It
https://github.com/DavidAbramian/refacing
공식 코드Refacing: reconstructing anonymized facial features using GANs
https://github.com/DavidVinicius/concatenating-videos-for-sign-language-translation
공식 코드Less is more: concatenating videos for Sign Language Translation from a small set of signs
https://github.com/DeepGraphLearning/ConfGF
공식 코드Learning Gradient Fields for Molecular Conformation Generation
https://github.com/DeepLearnXMU/ABD-NMT
공식 코드Asynchronous Bidirectional Decoding for Neural Machine Translation
https://github.com/DeepLearnXMU/MM-DCCN
공식 코드Dynamic Context-guided Capsule Network for Multimodal Machine Translation
https://github.com/DeepLearnXMU/Otem-Utem
공식 코드Otem&Utem: Over- and Under-Translation Evaluation Metric for NMT
https://github.com/DeepLearnXMU/User-Driven-NMT
공식 코드https://github.com/DeepLearnXMU/VNMT
공식 코드https://github.com/DeepMIALab/AI-FFPE
공식 코드https://github.com/DevSinghSachan/multilingual_nmt
공식 코드Parameter Sharing Methods for Multilingual Self-Attentional Translation Models
https://github.com/DianDYu/language_embeddings
공식 코드Language Embeddings for Typology and Cross-lingual Transfer Learning
https://github.com/DinoMan/DINO
공식 코드https://github.com/Disiok/poetry-seq2seq
공식 코드Hierarchical Attention: What Really Counts in Various NLP Tasks
https://github.com/Drkun/ZSTCI
공식 코드https://github.com/ELITR/wmt19-elitr-testsuite
공식 코드https://github.com/ESanchezLozano/GANnotation
공식 코드Triple consistency loss for pairing distributions in GAN-based face synthesis
https://github.com/EdinburghNLP/nematus
공식 코드https://github.com/EdinburghNLP/opus-100-corpus
공식 코드Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation
https://github.com/Edward-Sun/structured-nart
공식 코드https://github.com/ElanVB/optimal_transformer_depth
공식 코드On Optimal Transformer Depth for Low-Resource Language Translation
https://github.com/Elbria/xling-SemDiv-NMT
공식 코드https://github.com/ElementAI/picard
공식 코드PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models
https://github.com/ElliottYan/Multi_Unit_Transformer
공식 코드https://github.com/Elvinky/IEGAN
공식 코드Independent Encoder for Deep Hierarchical Unsupervised Image-to-Image Translation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.