Papers with Code 아카이브
Translation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 4
- 보존된 평가 기록
- 5
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/txsun1997/metric-fairness
SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation
https://github.com/tym002/landmark_detection
Volumetric landmark detection with a multi-scale shift equivariant neural network
https://github.com/tyxr/bert
https://github.com/tyy2022/usst_autosimultrans2022
https://github.com/u7javed/Bidirectional-Image-to-Image-Translator
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/u7javed/Transformer-Multi-Language-Translator
https://github.com/uakarsh/Eit-Enhanced-Interactive-Transformer
https://github.com/uam-biometrics/Transformer-Networks
https://github.com/ubc-nlp/coling2022
Translating the Unseen? Yoruba-English MT in Low-Resource, Morphologically-Unmarked Settings
https://github.com/ubc-tea/Improving-Fairness-in-Image-Classification-via-Sketching
Learning to generate line drawings that convey geometry and semantics
https://github.com/ucam-smt/sgnmt
SGNMT -- A Flexible NMT Decoding Platform for Quick Prototyping of New Models and Search Strategies
https://github.com/uchange/ulangel
https://github.com/uclanlp/NamedEntityLanguageModel
https://github.com/uclanlp/visualbert
Unsupervised Vision-and-Language Pre-training Without Parallel Images and Captions
https://github.com/ucsd-dsc-arts/dsc160-final-dsc160-final-group13
https://github.com/ucsd-ml-arts/ml-art-final-chandhini-g-1
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/ucsd-ml-arts/ml-art-final-justin-law
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/udnet96/BART-various-finetune
https://github.com/uds-lsv/menyo-20k_mt
The Effect of Domain and Diacritics in Yorùbá-English Neural Machine Translation
https://github.com/ufal/nmt-pe-effects-2021
Neural Machine Translation Quality and Post-Editing Performance
https://github.com/uhh-lt/amharicmodels
The Effect of Normalization for Bi-directional Amharic-English Neural Machine Translation
https://github.com/ulstu/ml
https://github.com/ulstu/robotics_ml
https://github.com/umeiko/mindspore-seq2seq
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
https://github.com/umutcanaltin/research
LeFlow: Enabling Flexible FPGA High-Level Synthesis of Tensorflow Deep Neural Networks
https://github.com/umutguneri/Question-Answering-Assistant
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/unbabel/mt-telescope
Scientific Credibility of Machine Translation Research: A Meta-Evaluation of 769 Papers
https://github.com/unconst/MACH
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
https://github.com/unicredit/ganzo
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/urvashik/knnmt
https://github.com/ut-vision/simhand
https://github.com/utunga/pix2pix-tensorflow
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/uzi0espil/research-papers-implementation/tree/master/Attention%20Is%20All%20You%20Need
https://github.com/uzi0espil/research-papers-implementation/tree/master/Effective%20Approaches%20to%20Attention-based%20Neural%20Machine%20Translation
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/uzi0espil/research-papers-implementation/tree/master/Neural%20Machine%20Translation%20by%20Jointly%20Learning%20to%20Align%20and%20Translate
Neural Machine Translation by Jointly Learning to Align and Translate
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.