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 3101–3125 of 3569 papers

TitleStatusHype
Fuzzy Tiling Activations: A Simple Approach to Learning Sparse Representations Online—0
Constructing Multiple Tasks for Augmentation: Improving Neural Image Classification With K-means FeaturesCode0
Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual RecognitionCode0
Six Degree-of-Freedom Body-Fixed Hovering over Unmapped Asteroids via LIDAR Altimetry and Reinforcement Meta-Learning—0
Enhanced Meta-Learning for Cross-lingual Named Entity Recognition with Minimal ResourcesCode0
Self-Supervised Learning For Few-Shot Image ClassificationCode0
Learning from the Past: Continual Meta-Learning via Bayesian Graph Modeling—0
Data Efficient Direct Speech-to-Text Translation with Modality Agnostic Meta-Learning—0
Meta Label Correction for Noisy Label LearningCode0
Learning to Few-Shot Learn Across Diverse Natural Language Classification TasksCode0
Learning to reinforcement learn for Neural Architecture SearchCode0
Fair Meta-Learning: Learning How to Learn Fairly—0
Shaping Visual Representations with Language for Few-shot ClassificationCode0
Addressing Ambiguity of Emotion Labels Through Meta-Learning—0
Learning One-Shot Imitation from Humans without HumansCode0
Text Emotion Distribution Learning from Small Sample: A Meta-Learning Approach—0
Seeded self-play for language learning—0
Learning to Customize Model Structures for Few-shot Dialogue Generation TasksCode0
RankML: a Meta Learning-Based Approach for Pre-Ranking Machine Learning Pipelines—0
Hierarchical Expert Networks for Meta-Learning—0
Meta-Learning to Cluster—0
When MAML Can Adapt Fast and How to Assist When It Cannot—0
Meta-learning for robust child-adult classification from speech—0
HIDRA: Head Initialization across Dynamic targets for Robust ArchitecturesCode0
Meta Learning for End-to-End Low-Resource Speech Recognition—0
Show:102550
← PrevPage 125 of 143Next →
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