https://github.com/IRVLUTD/Proto-CLIP
공식 코드Proto-CLIP: Vision-Language Prototypical Network for Few-Shot Learning
Papers with Code 아카이브
Meta-Learning
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Meta-learning is a methodology considered with "learning to learn" machine learning algorithms.
<span style="color:grey; opacity: 0.6">( Image credit: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/IRVLUTD/fewsol-toolkit
공식 코드FewSOL: A Dataset for Few-Shot Object Learning in Robotic Environments
https://github.com/IRVLUTD/meta-dataset
공식 코드FewSOL: A Dataset for Few-Shot Object Learning in Robotic Environments
https://github.com/IU-SAIGE/contrastive_mixtures
공식 코드Self-Supervised Learning from Contrastive Mixtures for Personalized Speech Enhancement
https://github.com/IdanAchituve/GP-Tree
공식 코드GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning
https://github.com/IoBT-VISTEC/MetaSleepLearner
공식 코드https://github.com/IraKorshunova/bruno
공식 코드https://github.com/IreneZihuiLi/TopicAttentionMedicalAD
공식 코드A Neural Topic-Attention Model for Medical Term Abbreviation Disambiguation
https://github.com/Ivanrs297/MACFE
공식 코드MACFE: A Meta-learning and Causality Based Feature Engineering Framework
https://github.com/JBall1/Few-Shot-Limited-Data
공식 코드https://github.com/JWSoh/MZSR
공식 코드https://github.com/JWoong148/MetaPerturb
공식 코드MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and Architectures
https://github.com/Jarvis73/FPTransPaddle
공식 코드https://github.com/JayC1208/Q-MAML
공식 코드Q-MAML: Quantum Model-Agnostic Meta-Learning for Variational Quantum Algorithms
https://github.com/JetRunner/MetaDistil
공식 코드BERT Learns to Teach: Knowledge Distillation with Meta Learning
https://github.com/Jia-Research-Lab/PFENet
공식 코드Prior Guided Feature Enrichment Network for Few-Shot Segmentation
https://github.com/JinA0218/drugood-densify
공식 코드Robust Molecular Property Prediction via Densifying Scarce Labeled Data
https://github.com/JinheonBaek/GEN
공식 코드Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction
https://github.com/JosephKJ/iOD
공식 코드https://github.com/JoyHuYY1412/LST_LVIS
공식 코드https://github.com/JunjieYang97/GGS-MAML-DL
공식 코드Theoretical Convergence of Multi-Step Model-Agnostic Meta-Learning
https://github.com/JunjieYang97/GGS-MAML-RL
공식 코드Theoretical Convergence of Multi-Step Model-Agnostic Meta-Learning
https://github.com/JunjieYang97/MRVRBO
공식 코드https://github.com/KEAML-JLU/Meta-GPS-Plus
공식 코드Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-Training
https://github.com/KEAML-JLU/SMILE
공식 코드Dual-level Mixup for Graph Few-shot Learning with Fewer Tasks
https://github.com/KaiyangZhou/Dassl.pytorch
공식 코드https://github.com/Kayzaks/VividNet
공식 코드A Neural-Symbolic Architecture for Inverse Graphics Improved by Lifelong Meta-Learning
https://github.com/KhrulkovV/hyperbolic-image-embeddings
공식 코드https://github.com/Khurramjaved96/mrcl
공식 코드https://github.com/KyraZzz/nlp-prompt-attack
공식 코드Revisiting Automated Prompting: Are We Actually Doing Better?
https://github.com/LIUYUANWEI98/NERTNet
공식 코드Learning Non-target Knowledge for Few-shot Semantic Segmentation
https://github.com/LONGXUANX/CDFormer_code
공식 코드CDFormer: Cross-Domain Few-Shot Object Detection Transformer Against Feature Confusion
https://github.com/Layjins/CECNet
공식 코드https://github.com/Layjins/FewShotLearning-tSF
공식 코드tSF: Transformer-based Semantic Filter for Few-Shot Learning
https://github.com/Layjins/SpatialFormer
공식 코드SpatialFormer: Semantic and Target Aware Attentions for Few-Shot Learning
https://github.com/LeanneNortje/direct_multimodal_few-shot_learning
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.