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 1001–1010 of 3569 papers

TitleStatusHype
ContrastNet: A Contrastive Learning Framework for Few-Shot Text ClassificationCode1
SuSana Distancia is all you need: Enforcing class separability in metric learning via two novel distance-based loss functions for few-shot image classification—0
Learning to Learn Unlearned Feature for Brain Tumor Segmentation—0
DAC-MR: Data Augmentation Consistency Based Meta-Regularization for Meta-LearningCode1
Meta-Optimization for Higher Model Generalizability in Single-Image Depth Prediction—0
Meta Omnium: A Benchmark for General-Purpose Learning-to-LearnCode1
Meta-hallucinator: Towards Few-Shot Cross-Modality Cardiac Image Segmentation—0
Meta-Learners for Few-Shot Weakly-Supervised Medical Image SegmentationCode0
Rethinking Class Imbalance in Machine Learning—0
Sequential Latent Variable Models for Few-Shot High-Dimensional Time-Series ForecastingCode1
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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