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 1001–1050 of 3569 papers

TitleStatusHype
RAMario: Experimental Approach to Reptile Algorithm -- Reinforcement Learning for MarioCode0
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
Black-box Prompt Tuning with Subspace Learning—0
Meta-Learning Enabled Score-Based Generative Model for 1.5T-Like Image Reconstruction from 0.5T MRI—0
Generative Meta-Learning for Zero-Shot Relation Triplet Extraction—0
Accelerating Neural Self-Improvement via Bootstrapping—0
Model-agnostic Measure of Generalization DifficultyCode0
META-SMGO-Δ: similarity as a prior in black-box optimization—0
Analogy-Forming Transformers for Few-Shot 3D Parsing—0
On the Generalization Error of Meta Learning for the Gibbs Algorithm—0
Implicit Counterfactual Data Augmentation for Robust Learning—0
Combining Adversaries with Anti-adversaries in Training—0
Master: Meta Style Transformer for Controllable Zero-Shot and Few-Shot Artistic Style Transfer—0
AwesomeMeta+: A Mixed-Prototyping Meta-Learning System Supporting AI Application Design AnywhereCode1
Policy Resilience to Environment Poisoning Attacks on Reinforcement Learning—0
Meta-tuning Loss Functions and Data Augmentation for Few-shot Object Detection—0
Constructing a meta-learner for unsupervised anomaly detection—0
Task-Adaptive Pseudo Labeling for Transductive Meta-Learning—0
A Meta-heuristic Approach to Estimate and Explain Classifier Uncertainty—0
DECN: Evolution Inspired Deep Convolution Network for Black-box Optimization—0
Normalizing Flow-based Neural Process for Few-Shot Knowledge Graph CompletionCode1
A Survey on Few-Shot Class-Incremental Learning—0
Meta-optimized Contrastive Learning for Sequential RecommendationCode1
Learning to Learn Group Alignment: A Self-Tuning Credo Framework with Multiagent Teams—0
Symbiotic Message Passing Model for Transfer Learning between Anti-Fungal and Anti-Bacterial Domains—0
Generalizable Deep Learning Method for Suppressing Unseen and Multiple MRI Artifacts Using Meta-learning—0
Meta-Learned Models of CognitionCode1
TinyReptile: TinyML with Federated Meta-Learning—0
RecUP-FL: Reconciling Utility and Privacy in Federated Learning via User-configurable Privacy Defense—0
Exploring Effective Factors for Improving Visual In-Context LearningCode1
Meta Compositional Referring Expression Segmentation—0
MERMAIDE: Learning to Align Learners using Model-Based Meta-Learning—0
Experience-Based Evolutionary Algorithms for Expensive OptimizationCode0
Theoretical Characterization of the Generalization Performance of Overfitted Meta-Learning—0
ForamViT-GAN: Exploring New Paradigms in Deep Learning for Micropaleontological Image Analysis—0
SPOT: Sequential Predictive Modeling of Clinical Trial Outcome with Meta-Learning—0
Learning to Learn with Indispensable Connections—0
Adaptive Ensemble Learning: Boosting Model Performance through Intelligent Feature Fusion in Deep Neural Networks—0
Meta-Learning with a Geometry-Adaptive PreconditionerCode1
VNE: An Effective Method for Improving Deep Representation by Manipulating Eigenvalue DistributionCode1
Online Algorithms for Hierarchical Inference in Deep Learning applications at the Edge—0
A comparison of small sample methods for Handshape RecognitionCode0
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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