SOTAVerified

Meta-Learning

Meta-learning is a methodology considered with "learning to learn" machine learning algorithms.

( Image credit: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks )

Papers

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Showing 2376–2400 of 3569 papers

TitleStatusHype
Multilingual and cross-lingual document classification: A meta-learning approachCode1
Similarity of Classification TasksCode0
Combat Data Shift in Few-shot Learning with Knowledge Graph—0
Meta-learning on Spectral Images of Electroencephalogram of Schizophenics—0
Meta Adversarial Training against Universal PatchesCode1
Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond—0
Efficient Graph Deep Learning in TensorFlow with tf_geometricCode1
Few-Shot Semantic Parsing for New PredicatesCode1
Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning AlgorithmsCode1
Meta-learning Based Beamforming Design for MISO Downlink—0
Meta-Learning for Effective Multi-task and Multilingual ModellingCode0
Meta-Regularization by Enforcing Mutual-ExclusivenessCode0
Contrastive Prototype Learning with Augmented Embeddings for Few-Shot Learning—0
An Information-Theoretic Analysis of the Impact of Task Similarity on Meta-Learning—0
Stress Testing of Meta-learning Approaches for Few-shot Learning—0
Few-shot Action Recognition with Prototype-centered Attentive LearningCode1
Learning Abstract Task Representations—0
Meta-Reinforcement Learning for Adaptive Motor Control in Changing Robot Dynamics and Environments—0
Magnification Generalization for Histopathology Image EmbeddingCode0
Robustness of Meta Matrix Factorization Against Strict Privacy ConstraintsCode0
Few Shot Dialogue State Tracking using Meta-learningCode1
Learning to Focus: Cascaded Feature Matching Network for Few-shot Image Recognition—0
A Brief Survey of Associations Between Meta-Learning and General AI—0
Lesion2Vec: Deep Metric Learning for Few-Shot Multiple Lesions Recognition in Wireless Capsule Endoscopy Video—0
Deep Interactive Bayesian Reinforcement Learning via Meta-Learning—0
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All datasetsML10MT50ML45OMNIGLOT - 1-Shot, 20-way

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8—Unverified
2MZMeta-train success rate97.6—Unverified
3MAMLMeta-test success rate36—Unverified
4RL^2Meta-test success rate10—Unverified
5DnCMeta-test success rate5.4—Unverified
6PEARLMeta-test success rate0—Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60—Unverified
2Multi-task multi-head SACAverage Success Rate35.85—Unverified
3DisCorAverage Success Rate26—Unverified
4NDPAverage Success Rate11—Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5—Unverified
2MZMeta-test success rate (zero-shot)17.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75—Unverified