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 1901–1925 of 3569 papers

TitleStatusHype
Set-to-Sequence Methods in Machine Learning: a Review—0
SGMNet: Scene Graph Matching Network for Few-Shot Remote Sensing Scene Classification—0
Sharing to learn and learning to share; Fitting together Meta-Learning, Multi-Task Learning, and Transfer Learning: A meta review—0
Short-Term Stock Price-Trend Prediction Using Meta-Learning—0
Short-term Traffic Prediction with Deep Neural Networks: A Survey—0
Should Models Be Accurate?—0
Siamese Meta-Learning and Algorithm Selection with 'Algorithm-Performance Personas' [Proposal]—0
Distance Metric-Based Learning with Interpolated Latent Features for Location Classification in Endoscopy Image and Video—0
Siamese Transformer Networks for Few-shot Image Classification—0
Side-aware Meta-Learning for Cross-Dataset Listener Diagnosis with Subjective Tinnitus—0
Signal Transformer: Complex-valued Attention and Meta-Learning for Signal Recognition—0
SimCompass: Using Deep Learning Word Embeddings to Assess Cross-level Similarity—0
SimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel Learning—0
Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey—0
Single Domain Dynamic Generalization for Iris Presentation Attack Detection—0
Single Neuromorphic Memristor closely Emulates Multiple Synaptic Mechanisms for Energy Efficient Neural Networks—0
Six Degree-of-Freedom Body-Fixed Hovering over Unmapped Asteroids via LIDAR Altimetry and Reinforcement Meta-Learning—0
Sketch3T: Test-Time Training for Zero-Shot SBIR—0
Skill-based Meta-Reinforcement Learning—0
SML: Semantic Meta-learning for Few-shot Semantic Segmentation—0
Socratic RL: A Novel Framework for Efficient Knowledge Acquisition through Iterative Reflection and Viewpoint Distillation—0
Soft Layer Selection with Meta-Learning for Zero-Shot Cross-Lingual Transfer—0
Solving Stochastic Compositional Optimization is Nearly as Easy as Solving Stochastic Optimization—0
SParse: Ko University Graph-Based Parsing System for the CoNLL 2018 Shared Task—0
Sparse Meta Networks for Sequential Adaptation and its Application to Adaptive Language Modelling—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