https://github.com/mengweiren/segmentation-renormalized-harmonization
공식 코드Segmentation-Renormalized Deep Feature Modulation for Unpaired Image Harmonization
Papers with Code 아카이브
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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/menyifang/dct-net
공식 코드DCT-Net: Domain-Calibrated Translation for Portrait Stylization
https://github.com/meta-metrics/metametrics
공식 코드MetaMetrics-MT: Tuning Meta-Metrics for Machine Translation via Human Preference Calibration
https://github.com/mfinzi/LieConv
공식 코드https://github.com/mfinzi/equivariant-MLP
공식 코드A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups
https://github.com/mgaido91/FBK-fairseq-ST
공식 코드Contextualized Translation of Automatically Segmented Speech
https://github.com/mgheini/xattn-transfer-for-mt
공식 코드Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation
https://github.com/mhh0318/unid3
공식 코드Unified Discrete Diffusion for Simultaneous Vision-Language Generation
https://github.com/mibastro/VPE
공식 코드Variational Prototyping-Encoder: One-Shot Learning with Prototypical Images
https://github.com/michaelrzhang/lookahead
공식 코드https://github.com/michellekappl/mt_gender_german
공식 코드https://github.com/microsoft/Bringing-Old-Photos-Back-to-Life
공식 코드https://github.com/microsoft/CoCosNet-v2
공식 코드CoCosNet v2: Full-Resolution Correspondence Learning for Image Translation
https://github.com/microsoft/ContextualSP
공식 코드https://github.com/microsoft/MASS
공식 코드MASS: Masked Sequence to Sequence Pre-training for Language Generation
https://github.com/microsoft/NeuralSpeech
공식 코드FastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech Recognition
https://github.com/microsoft/gpt-mt
공식 코드How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation
https://github.com/microsoft/inmt-lite
공식 코드https://github.com/microsoft/qlib/tree/main/examples/benchmarks/TCTS
공식 코드https://github.com/microsoft/reina
공식 코드https://github.com/microsoft/speecht5
공식 코드The YiTrans End-to-End Speech Translation System for IWSLT 2022 Offline Shared Task
https://github.com/microsoft/stochastic-mixture-of-experts
공식 코드Taming Sparsely Activated Transformer with Stochastic Experts
https://github.com/microsoft/torchscale
공식 코드https://github.com/microsoft/unilm
공식 코드XLM-E: Cross-lingual Language Model Pre-training via ELECTRA
https://github.com/microsoft/unilm/tree/master/deltalm
공식 코드https://github.com/microsoft/vert-papers/tree/master/papers/AdvPicker
공식 코드AdvPicker: Effectively Leveraging Unlabeled Data via Adversarial Discriminator for Cross-Lingual NER
https://github.com/microsoft/vert-papers/tree/master/papers/UniTrans
공식 코드https://github.com/microsofttranslator/gate
공식 코드GATE: A Challenge Set for Gender-Ambiguous Translation Examples
https://github.com/microsofttranslator/gemba
공식 코드Large Language Models Are State-of-the-Art Evaluators of Translation Quality
https://github.com/mid-push/irwgan
공식 코드Unaligned Image-to-Image Translation by Learning to Reweight
https://github.com/middlekisser/GMNMT
공식 코드A Novel Graph-based Multi-modal Fusion Encoder for Neural Machine Translation
https://github.com/midobal/OpenNMT-py
공식 코드Demonstration of a Neural Machine Translation System with Online Learning for Translators
https://github.com/midobal/mthd
공식 코드Two Demonstrations of the Machine Translation Applications to Historical Documents
https://github.com/midusi/lsa-t
공식 코드LSA-T: The first continuous Argentinian Sign Language dataset for Sign Language Translation
https://github.com/miemieyanga/ResiDualGAN-DRDG
공식 코드ResiDualGAN: Resize-Residual DualGAN for Cross-Domain Remote Sensing Images Semantic Segmentation
https://github.com/migalkin/SQuAD-es-mt
공식 코드El Departamento de Nosotros: How Machine Translated Corpora Affects Language Models in MRC Tasks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.