https://github.com/dinobby/ZS-BERT
공식 코드ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute Representation Learning
Papers with Code 아카이브
Transfer Learning
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Transfer Learning is a machine learning technique where a model trained on one task is re-purposed and fine-tuned for a related, but different task. The idea behind transfer learning is to leverage the knowledge learned from a pre-trained model to solve a new, but related problem. This can be useful in situations where there is limited data available to train a new model from scratch, or when the new task is similar enough to the original task that the pre-trained model can be adapted to the new problem with only minor modifications.
<span style="color:grey; opacity: 0.6">( Image credit: Subodh Malgonde )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/dipuk0506/SpinalNet
공식 코드https://github.com/disk0Dancer/rubert-finetuned-pos
공식 코드POS-tagging to highlight the skeletal structure of sentences
https://github.com/divamgupta/mttdsc
공식 코드Multi-task Learning for Target-dependent Sentiment Classification
https://github.com/divelab/AIRS
공식 코드Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
https://github.com/dkozlov/awesome-knowledge-distillation
공식 코드https://github.com/dmis-lab/bioasq8b
공식 코드Transferability of Natural Language Inference to Biomedical Question Answering
https://github.com/dmis-lab/demographic-prediction
공식 코드https://github.com/dmis-lab/kitchenscale
공식 코드KitchenScale: Learning to predict ingredient quantities from recipe contexts
https://github.com/dmitrav/pheno-ml
공식 코드https://github.com/dmitrijsk/attentionhtr
공식 코드AttentionHTR: Handwritten Text Recognition Based on Attention Encoder-Decoder Networks
https://github.com/dmitryako/aus10spectrograms
공식 코드Data-Efficient Classification of Birdcall Through Convolutional Neural Networks Transfer Learning
https://github.com/dmlc/gluon-cv
공식 코드Bag of Tricks for Image Classification with Convolutional Neural Networks
https://github.com/dockstring/dockstring
공식 코드DOCKSTRING: easy molecular docking yields better benchmarks for ligand design
https://github.com/dodohow1011/speechadvreprogram
공식 코드Neural Model Reprogramming with Similarity Based Mapping for Low-Resource Spoken Command Recognition
https://github.com/doem97/deblora
공식 코드Learning De-Biased Representations for Remote-Sensing Imagery
https://github.com/dogregor/pretrain_gnns_fakenewsnet
공식 코드https://github.com/doheejin/HiPAMA
공식 코드Hierarchical Pronunciation Assessment with Multi-Aspect Attention
https://github.com/dong-huo/srr-maxl
공식 코드https://github.com/dongbo811/uqformer
공식 코드A Unified Query-based Paradigm for Camouflaged Instance Segmentation
https://github.com/dongchen06/dmweeds
공식 코드https://github.com/dongpham120899/labelvariation_sciie
공식 코드Solving Label Variation in Scientific Information Extraction via Multi-Task Learning
https://github.com/dongxzz/roli
공식 코드https://github.com/dongyounglim/etheopoula
공식 코드https://github.com/dorlivne/PoPS
공식 코드PoPS: Policy Pruning and Shrinking for Deep Reinforcement Learning
https://github.com/dorltcheng/Transfer-Learning-U-Net-Deep-Learning-for-Lung-Ultrasound-Segmentation
공식 코드Transfer Learning U-Net Deep Learning for Lung Ultrasound Segmentation
https://github.com/doubtedsteam/eas
공식 코드https://github.com/douglas2code/text2seg
공식 코드Text2Seg: Remote Sensing Image Semantic Segmentation via Text-Guided Visual Foundation Models
https://github.com/doviledo/revisiting-transfer
공식 코드Revisiting Hidden Representations in Transfer Learning for Medical Imaging
https://github.com/doviledo/source-matters
공식 코드Source Matters: Source Dataset Impact on Model Robustness in Medical Imaging
https://github.com/dpanigra/irs-partition
공식 코드An Intrusion Response System utilizing Deep Q-Networks and System Partitions
https://github.com/dpetrini/two-views-classifier
공식 코드https://github.com/dqyzhwk/transkt
공식 코드Contrastive Cross-Course Knowledge Tracing via Concept Graph Guided Knowledge Transfer
https://github.com/dragonlzm/pave
공식 코드https://github.com/dralgroup/aio-ani
공식 코드All-in-one foundational models learning across quantum chemical levels
https://github.com/dralgroup/mlatom
공식 코드All-in-one foundational models learning across quantum chemical levels
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.