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Meta-Learning

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하위 태스크: 3
평가표: 4
보존된 평가 기록: 13

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://bitbucket.org/tsendeemts/metanet

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Meta Networks

https://git.tu-berlin.de/rsim/chnr

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An Unsupervised Cross-Modal Hashing Method Robust to Noisy Training Image-Text Correspondences in Remote Sensing

https://github.com/16337093/PnP-FSOD

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Instant Response Few-shot Object Detection with Meta Strategy and Explicit Localization Inference

https://github.com/1KE-JI/HierVerb

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Hierarchical Verbalizer for Few-Shot Hierarchical Text Classification

https://github.com/1adrianb/lasp

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LASP: Text-to-Text Optimization for Language-Aware Soft Prompting of Vision & Language Models

https://github.com/1adrianb/unsupervised-face-representation

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Pre-training strategies and datasets for facial representation learning

https://github.com/1lol/pmri_optimal_control

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Optimization-Based Deep learning methods for Magnetic Resonance Imaging Reconstruction and Synthesis

https://github.com/231sm/Low_Resource_KBP

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Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection

https://github.com/3DAgentWorld/Toolkit-for-Prompt-Compression

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PCToolkit: A Unified Plug-and-Play Prompt Compression Toolkit of Large Language Models

https://github.com/3dperceptionlab/toad

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Text-driven Online Action Detection

https://github.com/4rdhendu/fiml

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Fast Few-Shot Classification by Few-Iteration Meta-Learning

https://github.com/7231/Concept-free-Causal-Disentangle

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Concept-free Causal Disentanglement with Variational Graph Auto-Encoder

https://github.com/772922440/pgada

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PGADA: Perturbation-Guided Adversarial Alignment for Few-shot Learning Under the Support-Query Shift

https://github.com/AI-secure/Meta-Neural-Kernel

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Global Convergence and Generalization Bound of Gradient-Based Meta-Learning with Deep Neural Nets

https://github.com/AI-secure/multi-task-learning

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Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation

https://github.com/AIVResearch/MSANet

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MSANet: Multi-Similarity and Attention Guidance for Boosting Few-Shot Segmentation

https://github.com/AKC23/Harnessing-LLMs-over-transformer-models-for-detecting-Bengali-depressive-text-A-comprehensive-study

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Harnessing Large Language Models Over Transformer Models for Detecting Bengali Depressive Social Media Text: A Comprehensive Study

https://github.com/ANDYZAQ/GF-SAM

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Bridge the Points: Graph-based Few-shot Segment Anything Semantically

https://github.com/AceChuse/TaskLevelAug

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Task Augmentation by Rotating for Meta-Learning

https://github.com/Aerospace-AI/Aerospace-AI.github.io

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Reinforcement Meta-Learning for Interception of Maneuvering Exoatmospheric Targets with Parasitic Attitude Loop

https://github.com/Aghasemian/OptimalLinkPrediction

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Stacking Models for Nearly Optimal Link Prediction in Complex Networks

https://github.com/AhmedFrikha/ARCADe-A-Rapid-Continual-Anomaly-Detector

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ARCADe: A Rapid Continual Anomaly Detector

https://github.com/AhmedFrikha/Few-Shot-One-Class-Classification-via-Meta-Learning

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Few-Shot One-Class Classification via Meta-Learning

https://github.com/Alejandro-Galan/Few-shot_COVID-19_Inter-vs-Intra

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Few-shot learning for COVID-19 Chest X-Ray Classification with Imbalanced Data: An Inter vs. Intra Domain Study

https://github.com/AlexImb/automl-streams

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Automated Machine Learning Techniques for Data Streams

https://github.com/AlexanderBartler/MT3

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MT3: Meta Test-Time Training for Self-Supervised Test-Time Adaption

https://github.com/Alibaba-AAIG/SSL-FEW-SHOT

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Self-Supervised Learning For Few-Shot Image Classification

https://github.com/Alibaba-NLP/ProtoRE

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Prototypical Representation Learning for Relation Extraction

https://github.com/AlibabaPAI/one_shot_text_labeling

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One-shot Text Field Labeling using Attention and Belief Propagation for Structure Information Extraction

https://github.com/AliciaCurth/CATENets

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Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms

https://github.com/AllanYangZhou/metalearning-symmetries

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Meta-Learning Symmetries by Reparameterization

https://github.com/Amgao/RLS-RTMDNet

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Recursive Least-Squares Estimator-Aided Online Learning for Visual Tracking

https://github.com/AmmarKamoona/RFS-Energy-Anomaly-Detection-of-Defect

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Anomaly Detection of Defect using Energy of Point Pattern Features within Random Finite Set Framework

https://github.com/Ant-Brain/EfficientWord-Net

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EfficientWord-Net: An Open Source Hotword Detection Engine based on One-shot Learning

https://github.com/AntreasAntoniou/FewShotContinualLearning

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Defining Benchmarks for Continual Few-Shot Learning

https://github.com/Arashkhajooei/Meta-Learning-Integration-in-Hierarchical-Reinforcement-Learning-for-Advanced-Task-Complexity

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Meta-Learning Integration in Hierarchical Reinforcement Learning for Advanced Task Complexity

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