https://github.com/NamyeongK/USA_UFGSM
공식 코드Repurposing Pretrained Models for Robust Out-of-domain 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/Near32/Regym/tree/develop/benchmark/R2D2/SymbolicBehaviourBenchmark
공식 코드Meta-Referential Games to Learn Compositional Learning Behaviours
https://github.com/NesterukSergey/segmentation_image_augmentation
공식 코드Image Augmentation for Multitask Few-Shot Learning: Agricultural Domain Use-Case
https://github.com/NeuralDynamicsAndComputing/MetaLearning-Plasticity
공식 코드Meta-Learning Biologically Plausible Plasticity Rules with Random Feedback Pathways
https://github.com/Neutralzz/RefQA
공식 코드Harvesting and Refining Question-Answer Pairs for Unsupervised QA
https://github.com/Nic5472K/MetaLearnCC2021_AAAI_MetaSGD-CL
공식 코드Learning to Continually Learn Rapidly from Few and Noisy Data
https://github.com/Nithin-Holla/MetaLifelongLanguage
공식 코드Meta-Learning with Sparse Experience Replay for Lifelong Language Learning
https://github.com/Nithin-Holla/MetaWSD
공식 코드Learning to Learn to Disambiguate: Meta-Learning for Few-Shot Word Sense Disambiguation
https://github.com/NonlinearArtificialIntelligenceLab/jaxDiversity
공식 코드Neuronal diversity can improve machine learning for physics and beyond
https://github.com/OasisYang/SSG
공식 코드https://github.com/OctThe16th/FIGR
공식 코드https://github.com/Octavio-Pappalardo/Meta-learning-rewards
공식 코드Black box meta-learning intrinsic rewards for sparse-reward environments
https://github.com/Otsuzuki/Meta-learning-of-Pooling-Layers-for-Character-Recognition
공식 코드https://github.com/Ouasfi/FS-SDF
공식 코드Few 'Zero Level Set'-Shot Learning of Shape Signed Distance Functions in Feature Space
https://github.com/PGijsbers/symbolicdefaults
공식 코드https://github.com/PaddlePaddle/PaddleNLP/tree/develop/model_zoo/ernie-3.0
공식 코드ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation
https://github.com/Pang-Yatian/Point-MAE
공식 코드Masked Autoencoders for Point Cloud Self-supervised Learning
https://github.com/PolyAI-LDN/task-specific-datasets
공식 코드Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational Representations
https://github.com/PongNJ/V-RoAst
공식 코드V-RoAst: Visual Road Assessment. Can VLM be a Road Safety Assessor Using the iRAP Standard?
https://github.com/Prasath2001/Party-Symbol-Classifier
공식 코드https://github.com/PrincetonLIPS/randomized_telescopes
공식 코드Efficient Optimization of Loops and Limits with Randomized Telescoping Sums
https://github.com/Programmergg/NTK-FSCIL
공식 코드https://github.com/Pulkit-Khandelwal/medical-mldg-seg
공식 코드Domain Generalizer: A Few-shot Meta Learning Framework for Domain Generalization in Medical Imaging
https://github.com/PwnerHarry/META
공식 코드META-Learning State-based Eligibility Traces for More Sample-Efficient Policy Evaluation
https://github.com/PwnerHarry/MTA
공식 코드META-Learning State-based Eligibility Traces for More Sample-Efficient Policy Evaluation
https://github.com/QIU-Hongxiang-David/Gamma-minimax-learninng
공식 코드Adversarial Meta-Learning of Gamma-Minimax Estimators That Leverage Prior Knowledge
https://github.com/RS2002/CrossFi
공식 코드CrossFi: A Cross Domain Wi-Fi Sensing Framework Based on Siamese Network
https://github.com/RS2002/KNN-MMD
공식 코드KNN-MMD: Cross Domain Wireless Sensing via Local Distribution Alignment
https://github.com/RamnathKumar181/Task-Diversity-meta-learning
공식 코드https://github.com/Ramprasad-Group/copolymer_informatics
공식 코드https://github.com/RaphaelBensTAU/MetaInternalLearning
공식 코드https://github.com/Reagan1311/ASGNet
공식 코드Adaptive Prototype Learning and Allocation for Few-Shot Segmentation
https://github.com/ReginaMayhem/ClusterBusters
공식 코드Meta-Learning for One-Class Classification with Few Examples using Order-Equivariant Network
https://github.com/RenkunNi/MetaAug
공식 코드https://github.com/Reza-esfandiarpoor/pseudo-shots
공식 코드Extended Few-Shot Learning: Exploiting Existing Resources for Novel Tasks
https://github.com/Rondorf/BOReL
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.