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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3376–3400 of 3569 papers

TitleStatusHype
Meta-Learning Mean Functions for Gaussian Processes—0
FIGR: Few-shot Image Generation with ReptileCode0
Learning to Design RNACode0
Meta Reinforcement Learning with Distribution of Exploration Parameters Learned by Evolution Strategies—0
An Investigation of Few-Shot Learning in Spoken Term ClassificationCode0
Meta Architecture SearchCode0
Learning to Navigate the Web—0
Unsupervised Meta-learning of Figure-Ground Segmentation via Imitating Visual EffectsCode0
Toward Multimodal Model-Agnostic Meta-Learning—0
Deep Online Learning via Meta-Learning: Continual Adaptation for Model-Based RL—0
Few-shot classification in Named Entity Recognition TaskCode0
Reconciling meta-learning and continual learning with online mixtures of tasks—0
Learning to Learn from Noisy Labeled DataCode0
Face-Focused Cross-Stream Network for Deception Detection in Videos—0
Prior-Knowledge and Attention-based Meta-Learning for Few-Shot Learning—0
The effects of negative adaptation in Model-Agnostic Meta-Learning—0
Meta Learning Deep Visual Words for Fast Video Object SegmentationCode0
MetaReg: Towards Domain Generalization using Meta-Regularization—0
Learning To Learn Around A Common Mean—0
Recurrent machines for likelihood-free inferenceCode0
One-at-a-time: A Meta-Learning Recommender-System for Recommendation-Algorithm Selection on Micro Level—0
Formulating Camera-Adaptive Color Constancy as a Few-shot Meta-Learning Problem—0
Unsupervised Meta-Learning For Few-Shot Image Classification—0
ParsRec: A Novel Meta-Learning Approach to Recommending Bibliographic Reference Parsers—0
Representation based and Attention augmented 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