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/ManasRMohanty/DS5500-capstone
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
https://github.com/Marigold/universal-portfolios
https://github.com/Mathieu-Seurin/impact-driven-exploration
Learning with AMIGo: Adversarially Motivated Intrinsic Goals
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Learned_Initializations_for_Optimizing_Coordinate_Based_Neural_Representations
Learned Initializations for Optimizing Coordinate-Based Neural Representations
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Model_Agnostic_Meta_Learning_for_Fast_Adaptation_of_Deep_Networks
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/On_First_Order_Meta_Learning_Algorithms
https://github.com/MeRajat/SolvingAlmostAnythingWithBert
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
https://github.com/Meidianwen/few_shot_segmentation
Adaptive Prototype Learning and Allocation for Few-Shot Segmentation
https://github.com/Michedev/Prototypical-Networks
https://github.com/Michedev/snail
https://github.com/MillendRoy/one-shot-reinforcement-learning-classification
Deep Reinforcement One-Shot Learning for Artificially Intelligent Classification Systems
https://github.com/Mind23-2/MindCode-101/tree/main/MAML
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/Mind23-2/MindCode-101/tree/main/MaskedFaceRecognition
https://github.com/Mind23-2/MindCode-101/tree/main/meta-baseline
Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning
https://github.com/Mind23-2/MindCode-138
https://github.com/Mind23-2/MindCode-140
https://github.com/Mind23-2/MindCode-3/tree/main/MAML
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/Mind23-2/MindCode-3/tree/main/MaskedFaceRecognition
https://github.com/Mind23-2/MindCode-3/tree/main/m2det
Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning
https://github.com/Mind23-2/MindCode-3/tree/main/meta-baseline
Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning
https://github.com/Mind23-2/MindCode-5/tree/main/ProtoNet
https://github.com/Mind23-2/MindCode-55
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/Mind23-2/MindCode-66
https://github.com/MindCode-4/code-11/tree/main/comparing-the-efficacy
Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy Imitation
https://github.com/MindCode-4/code-12/tree/main/learning-an-explicit-hyperparameter
Learning an Explicit Hyperparameter Prediction Function Conditioned on Tasks
https://github.com/MindCode-4/code-3/tree/main/deta
https://github.com/MindCode-4/code-4/tree/main/plbart
NatGen: Generative pre-training by "Naturalizing" source code
https://github.com/MindCode-4/code-6/tree/main/comparing-the-efficacy
Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy Imitation
https://github.com/MindCode-4/code-7/tree/main/lcn-master-ms
https://github.com/MindCode-4/code-7/tree/main/learning-an-explicit-hyperparameter
https://github.com/MindCode-4/code-8/tree/main/privacy-enhancement
https://github.com/MindSpore-paper-code-2/code3/tree/main/TNT
TNT: Text-Conditioned Network with Transductive Inference for Few-Shot Video Classification
https://github.com/MindSpore-paper-code-2/code3/tree/main/unisiam
https://github.com/MindSpore-paper-code-3/code5/tree/main/RNA
https://github.com/MindSpore-paper-code-3/code6/tree/main/ats
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.