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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/rayneholland/collabdm
공식 코드https://github.com/rpeloff/multimodal-one-shot-learning
공식 코드https://github.com/rudylyh/CTN_data
공식 코드Contour Transformer Network for One-shot Segmentation of Anatomical Structures
https://github.com/scutcyr/soulchat2.0
공식 코드https://github.com/sgottsch/tab2kg
공식 코드Tab2KG: Semantic Table Interpretation with Lightweight Semantic Profiles
https://github.com/shruti-jadon/Hands-on-One-Shot-Learning
공식 코드An Overview of Deep Learning Architectures in Few-Shot Learning Domain
https://github.com/shruti-jadon/Siamese-Network-for-One-shot-Learning
공식 코드Improving Siamese Networks for One Shot Learning using Kernel Based Activation functions
https://github.com/tankche1/IDeMe-Net
공식 코드https://github.com/tankche1/Semantic-Feature-Augmentation-in-Few-shot-Learning
공식 코드Multi-level Semantic Feature Augmentation for One-shot Learning
https://github.com/tensorflow/models
공식 코드https://github.com/typilus/typilus
공식 코드https://github.com/uber/ludwig
공식 코드https://github.com/uncbiag/easyreg
공식 코드Anatomical Data Augmentation via Fluid-based Image Registration
https://github.com/utkarshnath/Adjoint-Network
공식 코드Adjoined Networks: A Training Paradigm with Applications to Network Compression
https://github.com/wxie2013/gdfree-supervised-snn-mnist
공식 코드https://github.com/y1ny/WordDeletion
공식 코드Active Use of Latent Constituency Representation in both Humans and Large Language Models
https://github.com/yaniker/one-shot-skill-assessment-in-high-stakes-domains-with-limited-data-via-meta-learning
공식 코드One-shot skill assessment in high-stakes domains with limited data via meta learning
https://github.com/yingutk/NL-MAT
공식 코드Non-Local Representation based Mutual Affine-Transfer Network for Photorealistic Stylization
https://github.com/2023-MindSpore-4/Code-5/tree/main/MAML
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/AntreasAntoniou/HowToTrainYourMAMLPytorch
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/AntreasAntoniou/MatchingNetworks
https://github.com/Arnavadi19/siamese-one-shot
https://github.com/ArnoutDevos/maml-cifar-fs
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/BBDrive/Meta-SGD-RL
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/CAVED123/omniglot-dataset
https://github.com/CLEANit/SRS2021
https://github.com/COAOX/Cifar-Prototypical
https://github.com/CocoJam/MAML
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/DrMMZ/ProtoNet
https://github.com/Gaopeng-Bai/MANN_model
https://github.com/GeorgeDUT/MetaRLSAS
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/Guang000/Awesome-Dataset-Distillation/blob/main/README.md
https://github.com/HenryCWoo/LTLProject
https://github.com/HilbertXu/MAML-Tensorflow
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/Hsankesara/DeepResearch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.