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/Ramprasad-Group/copolymer_informatics
공식 코드https://github.com/RaphaelPellegrin/Transfer-Learning-with-PINNs-for-Efficient-Simulation-of-Branched-Flows
공식 코드Transfer Learning with Physics-Informed Neural Networks for Efficient Simulation of Branched Flows
https://github.com/RasaHQ/STAR
공식 코드https://github.com/Rec4Fun/RaMen
공식 코드RaMen: Multi-Strategy Multi-Modal Learning for Bundle Construction
https://github.com/Renchunzi-Xie/AOMSDA
공식 코드https://github.com/Reza-esfandiarpoor/pseudo-shots
공식 코드Extended Few-Shot Learning: Exploiting Existing Resources for Novel Tasks
https://github.com/RiTUAL-UH/cs_elmo
공식 코드From English to Code-Switching: Transfer Learning with Strong Morphological Clues
https://github.com/Rorozhl/CA-MKD
공식 코드https://github.com/RubaP/Ex-Claim
공식 코드Entity-aware Cross-lingual Claim Detection for Automated Fact-checking
https://github.com/RuiqingDing/SPSSOT
공식 코드Cross-hospital Sepsis Early Detection via Semi-supervised Optimal Transport with Self-paced Ensemble
https://github.com/RyanWangZf/SurvTRACE
공식 코드SurvTRACE: Transformers for Survival Analysis with Competing Events
https://github.com/SHI-Labs/Semi-Supervised-Transfer-Learning
공식 코드Adaptive Consistency Regularization for Semi-Supervised Transfer Learning
https://github.com/SJYuCNEL/Bregman-Correntropy-Conditional-Divergence
공식 코드Measuring the Discrepancy between Conditional Distributions: Methods, Properties and Applications
https://github.com/SLAMPAI/large-scale-pretraining-transfer
공식 코드https://github.com/SStarCCat/HM4SR
공식 코드Hierarchical Time-Aware Mixture of Experts for Multi-Modal Sequential Recommendation
https://github.com/STORMTROOPERRR/RSISCD
공식 코드A Late-Stage Bitemporal Feature Fusion Network for Semantic Change Detection
https://github.com/SY-Xuan/IICS
공식 코드Intra-Inter Camera Similarity for Unsupervised Person Re-Identification
https://github.com/SYangDong/tse-t
공식 코드Temporal Self-Ensembling Teacher for Semi-Supervised Object Detection
https://github.com/SaadAhmedJamal/IEEE_DFC2023
공식 코드Data Fusion for Multi-Task Learning of Building Extraction and Height Estimation
https://github.com/SaminYeasar/Off_Policy_Adversarial_Inverse_Reinforcement_Learning
공식 코드https://github.com/Sanghyun-Hong/DeepRecon
공식 코드Security Analysis of Deep Neural Networks Operating in the Presence of Cache Side-Channel Attacks
https://github.com/Sanyuan-Chen/RecAdam
공식 코드Recall and Learn: Fine-tuning Deep Pretrained Language Models with Less Forgetting
https://github.com/Saumajit/BanglaNLP/tree/main/Task_2
공식 코드https://github.com/SchafferZhang/DADA
공식 코드DADA: Deep Adversarial Data Augmentation for Extremely Low Data Regime Classification
https://github.com/SciML/NeuralPDE.jl
공식 코드Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next
https://github.com/SendongZhao/Multi-Task-Learning-for-MER-and-MEN
공식 코드https://github.com/SenticNet/conv-emotion
공식 코드https://github.com/SeungoneKim/SICK_Summarization
공식 코드Mind the Gap! Injecting Commonsense Knowledge for Abstractive Dialogue Summarization
https://github.com/Sha-Lab/CASTLE
공식 코드Learning Adaptive Classifiers Synthesis for Generalized Few-Shot Learning
https://github.com/Sha-Lab/FEAT
공식 코드Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions
https://github.com/ShampooWang/SpeechCLIP_plus
공식 코드https://github.com/ShangtongZhang/DeepRL
공식 코드DAC: The Double Actor-Critic Architecture for Learning Options
https://github.com/ShaniGam/RL-GAN
공식 코드Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation
https://github.com/ShannonAI/glyce
공식 코드https://github.com/ShaohuiLin/SSR
공식 코드Towards Compact ConvNets via Structure-Sparsity Regularized Filter Pruning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.