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One-Shot Learning
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One-shot learning is the task of learning information about object categories from a single training example.
<span style="color:grey; opacity: 0.6">( Image credit: Siamese Neural Networks for One-shot Image Recognition )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Amgao/RLS-RTMDNet
공식 코드Recursive Least-Squares Estimator-Aided Online Learning for Visual Tracking
https://github.com/Ant-Brain/EfficientWord-Net
공식 코드EfficientWord-Net: An Open Source Hotword Detection Engine based on One-shot Learning
https://github.com/AyanKumarBhunia/Deep-One-Shot-Logo-Retrieval
공식 코드https://github.com/Cerenaut/aha
공식 코드https://github.com/Darthholi/similarity-models
공식 코드Learning from similarity and information extraction from structured documents
https://github.com/Dongqi-Fan/OnePoseTrans
공식 코드One-Shot Learning for Pose-Guided Person Image Synthesis in the Wild
https://github.com/Learning-and-Intelligent-Systems/Analytical-Learning-Theory
공식 코드Generalization in Machine Learning via Analytical Learning Theory
https://github.com/Learning-and-Intelligent-Systems/DualCutout
공식 코드Generalization in Machine Learning via Analytical Learning Theory
https://github.com/OasisYang/SSG
공식 코드https://github.com/RS2002/CrossFi
공식 코드CrossFi: A Cross Domain Wi-Fi Sensing Framework Based on Siamese Network
https://github.com/ToFec/OneShotImageRegistration
공식 코드One Shot Learning for Deformable Medical Image Registration and Periodic Motion Tracking
https://github.com/a-moin/flexemg
공식 코드https://github.com/adipandas/one-shot-steel-surfaces
공식 코드One-Shot Recognition of Manufacturing Defects in Steel Surfaces
https://github.com/adriangonz/statistical-nlp-17
공식 코드https://github.com/alfassy/FETA
공식 코드FETA: Towards Specializing Foundation Models for Expert Task Applications
https://github.com/amgao/rls-onlinetrack
공식 코드Recursive Least-Squares Estimator-Aided Online Learning for Visual Tracking
https://github.com/andrewbo29/mtm-meta-learning-sa
공식 코드Simultaneous Perturbation Method for Multi-Task Weight Optimization in One-Shot Meta-Learning
https://github.com/anonymous4545/jers
공식 코드One-shot Joint Extraction, Registration and Segmentation of Neuroimaging Data
https://github.com/antonpuz/DeROL
공식 코드Deep Reinforcement One-Shot Learning for Artificially Intelligent Classification Systems
https://github.com/apple2373/MetaIRNet
공식 코드Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual Recognition
https://github.com/arjish/meta-meta-classification
공식 코드https://github.com/ashwinpathak20/idiomaticity_detection_using_few_shot_learning
공식 코드https://github.com/atapour/ransomware-classification
공식 코드https://github.com/avm-debatr/bagganhq
공식 코드Self-Supervised One-Shot Learning for Automatic Segmentation of StyleGAN Images
https://github.com/avm-debatr/ganecdotes
공식 코드Self-Supervised One-Shot Learning for Automatic Segmentation of StyleGAN Images
https://github.com/avyavkumar/few-shot-learning-notebooks
공식 코드Learning New Tasks from a Few Examples with Soft-Label Prototypes
https://github.com/basiralab/multigraphgnet
공식 코드https://github.com/basiralab/one-representative-shot-learning
공식 코드https://github.com/bethgelab/siamese-mask-rcnn
공식 코드https://github.com/bosonphoton/abstractedgaussianprototypes
공식 코드Abstracted Gaussian Prototypes for One-Shot Concept Learning
https://github.com/brendenlake/omniglot
공식 코드https://github.com/buoncubi/fuzzy_sit
공식 코드https://github.com/cbfinn/maml
공식 코드Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/cbfinn/maml_rl
공식 코드Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/chuxuzhang/AAAI2020_FSRL
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.