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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
- 5
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ybai-nlp/cls_cr
공식 코드Unifying Cross-lingual Summarization and Machine Translation with Compression Rate
https://github.com/ybisk/charNMT-noise
공식 코드Synthetic and Natural Noise Both Break Neural Machine Translation
https://github.com/ygan/natsql
공식 코드Natural SQL: Making SQL Easier to Infer from Natural Language Specifications
https://github.com/ygan/spider-dk
공식 코드Exploring Underexplored Limitations of Cross-Domain Text-to-SQL Generalization
https://github.com/ygjwd12345/BiMix
공식 코드Bi-Mix: Bidirectional Mixing for Domain Adaptive Nighttime Semantic Segmentation
https://github.com/ygjwd12345/GLANet
공식 코드Global and Local Alignment Networks for Unpaired Image-to-Image Translation
https://github.com/yhlleo/DWC-GAN
공식 코드Describe What to Change: A Text-guided Unsupervised Image-to-Image Translation Approach
https://github.com/yhlleo/GMM-UNIT
공식 코드https://github.com/yhlleo/RG-UNIT
공식 코드Retrieval Guided Unsupervised Multi-domain Image-to-Image Translation
https://github.com/yhlleo/TriangleGAN
공식 코드Gesture-to-Gesture Translation in the Wild via Category-Independent Conditional Maps
https://github.com/yhlleo/stylegan-mmuit
공식 코드ISF-GAN: An Implicit Style Function for High-Resolution Image-to-Image Translation
https://github.com/yifding/hetseq
공식 코드HetSeq: Distributed GPU Training on Heterogeneous Infrastructure
https://github.com/yigitgunduc/tensor-to-image
공식 코드Tensor-to-Image: Image-to-Image Translation with Vision Transformers
https://github.com/yigitgunduc/vit-gan
공식 코드Vit-GAN: Image-to-image Translation with Vision Transformes and Conditional GANS
https://github.com/yihongl1u/xlm_lang_dis
공식 코드On the Copying Problem of Unsupervised NMT: A Training Schedule with a Language Discriminator Loss
https://github.com/yihuazhu111/3h-th
공식 코드3D Rotation and Translation for Hyperbolic Knowledge Graph Embedding
https://github.com/yikaiw/AsymFusion
공식 코드Learning Deep Multimodal Feature Representation with Asymmetric Multi-layer Fusion
https://github.com/yikaiw/CEN
공식 코드https://github.com/yilinyang7/fairseq_multi_fix
공식 코드Improving Multilingual Translation by Representation and Gradient Regularization
https://github.com/yilmazkorkmaz1/i2i-galip
공식 코드I2I-Galip: Unsupervised Medical Image Translation Using Generative Adversarial CLIP
https://github.com/yimeng0701/CKD_pytorch
공식 코드Why Skip If You Can Combine: A Simple Knowledge Distillation Technique for Intermediate Layers
https://github.com/yimingxu24/cldg
공식 코드https://github.com/yinaoxiong/gaslt
공식 코드https://github.com/yinzhangyue/c2fm
공식 코드https://github.com/yinzhangyue/smoothing-r3
공식 코드https://github.com/yixinL7/Direct-Style-Transfer
공식 코드https://github.com/yizhilll/transher
공식 코드TranSHER: Translating Knowledge Graph Embedding with Hyper-Ellipsoidal Restriction
https://github.com/ykykyk112/STIG
공식 코드https://github.com/yli1/CGPS
공식 코드https://github.com/ymcui/Cross-Lingual-MRC
공식 코드https://github.com/ymoslem/WLAC
공식 코드Translation Word-Level Auto-Completion: What can we achieve out of the box?
https://github.com/ymoslem/adaptive-mt-llm
공식 코드https://github.com/ymoslem/adaptive-mt-llm-fine-tuning
공식 코드Fine-tuning Large Language Models for Adaptive Machine Translation
https://github.com/ymoslem/model-compression
공식 코드Efficient Speech Translation through Model Compression and Knowledge Distillation
https://github.com/ymoslem/mt-lm
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.