https://github.com/Mlair77/nmt_adequacy
공식 코드Prevent the Language Model from being Overconfident in Neural Machine Translation
Papers with Code 아카이브
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MoPl90/image_translation
공식 코드Image Translation for Medical Image Generation -- Ischemic Stroke Lesions
https://github.com/MohammadRSalmanpour/MRI-to-US-Translation_MICCAI
공식 코드https://github.com/Moyu-42/Towards-A-Deep-Understanding-of-Multilingual-E2E-ST
공식 코드Towards a Deep Understanding of Multilingual End-to-End Speech Translation
https://github.com/Mukaffi28/Vashantor-A-Large-scale-Multilingual-Benchmark-Dataset
공식 코드https://github.com/Muzammal-Naseer/NRP
공식 코드https://github.com/MysteryVaibhav/robust_mtnt
공식 코드Improving Robustness of Machine Translation with Synthetic Noise
https://github.com/NJUNLP/MT-Patcher
공식 코드https://github.com/NJUNLP/x-LLM
공식 코드Extrapolating Large Language Models to Non-English by Aligning Languages
https://github.com/NLP-Playground/LaSS
공식 코드Learning Language Specific Sub-network for Multilingual Machine Translation
https://github.com/NLP2CT/Difficulty-Aware-MT-Evaluation
공식 코드https://github.com/NLP2CT/Meta-Curriculum
공식 코드Meta-Curriculum Learning for Domain Adaptation in Neural Machine Translation
https://github.com/NLP2CT/Unsupervised_Dialect_Translation
공식 코드Unsupervised Neural Dialect Translation with Commonality and Diversity Modeling
https://github.com/NLP2CT/norm-nmt
공식 코드Norm-Based Curriculum Learning for Neural Machine Translation
https://github.com/NVIDIA/NeMo
공식 코드Word Level Timestamp Generation for Automatic Speech Recognition and Translation
https://github.com/NVIDIA/OpenSeq2Seq
공식 코드Mixed-Precision Training for NLP and Speech Recognition with OpenSeq2Seq
https://github.com/NVlabs/FUNIT
공식 코드https://github.com/NVlabs/PoseRBPF
공식 코드PoseRBPF: A Rao-Blackwellized Particle Filter for 6D Object Pose Tracking
https://github.com/Nalin21478/NLP-Project
공식 코드Sentiment Analysis Across Languages: Evaluation Before and After Machine Translation to English
https://github.com/NavidRajabi/EMQA
공식 코드Towards More Equitable Question Answering Systems: How Much More Data Do You Need?
https://github.com/NetEase-GameAI/SPatchGAN
공식 코드SPatchGAN: A Statistical Feature Based Discriminator for Unsupervised Image-to-Image Translation
https://github.com/NeuralEnsemble/MotionClouds
공식 코드https://github.com/Neutralzz/syntax_aware_local_attention
공식 코드https://github.com/NickRuiz/power-asr
공식 코드https://github.com/Niger-Volta-LTI/yoruba-adr
공식 코드Attentive Sequence-to-Sequence Learning for Diacritic Restoration of Yorùbá Language Text
https://github.com/NiuTrans/LaMaTE
공식 코드Beyond Decoder-only: Large Language Models Can be Good Encoders for Machine Translation
https://github.com/NiuTrans/NiuTrans.NMT
공식 코드https://github.com/NoaKel/Noise-Aware-Alignment
공식 코드https://github.com/Noodles-321/Registration
공식 코드Is Image-to-Image Translation the Panacea for Multimodal Image Registration? A Comparative Study
https://github.com/NurIren/Gender-Bias-in-TR-to-EN-MT-Models
공식 코드Examining Covert Gender Bias: A Case Study in Turkish and English Machine Translation Models
https://github.com/OSU-STARLAB/Simul-LLM/tree/main/examples/simulmask
공식 코드https://github.com/OmarSZamzam/PixelShuffler
공식 코드PixelShuffler: A Simple Image Translation Through Pixel Rearrangement
https://github.com/OndrejTexler/Few-Shot-Patch-Based-Training
공식 코드Interactive Video Stylization Using Few-Shot Patch-Based Training
https://github.com/Oneplus/twpipe
공식 코드https://github.com/Onr/Council-GAN
공식 코드https://github.com/OpenNMT/OpenNMT
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.