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

Showing 10011025 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 classification0
Learning to Learn Unlearned Feature for Brain Tumor Segmentation0
DAC-MR: Data Augmentation Consistency Based Meta-Regularization for Meta-LearningCode1
Meta Omnium: A Benchmark for General-Purpose Learning-to-LearnCode1
Meta-Optimization for Higher Model Generalizability in Single-Image Depth Prediction0
Meta-hallucinator: Towards Few-Shot Cross-Modality Cardiac Image Segmentation0
Meta-Learners for Few-Shot Weakly-Supervised Medical Image SegmentationCode0
Rethinking Class Imbalance in Machine Learning0
Sequential Latent Variable Models for Few-Shot High-Dimensional Time-Series ForecastingCode1
Black-box Prompt Tuning with Subspace Learning0
Meta-Learning Enabled Score-Based Generative Model for 1.5T-Like Image Reconstruction from 0.5T MRI0
Generative Meta-Learning for Zero-Shot Relation Triplet Extraction0
Accelerating Neural Self-Improvement via Bootstrapping0
Model-agnostic Measure of Generalization DifficultyCode0
META-SMGO-Δ: similarity as a prior in black-box optimization0
Analogy-Forming Transformers for Few-Shot 3D Parsing0
On the Generalization Error of Meta Learning for the Gibbs Algorithm0
Implicit Counterfactual Data Augmentation for Robust Learning0
Combining Adversaries with Anti-adversaries in Training0
Policy Resilience to Environment Poisoning Attacks on Reinforcement Learning0
AwesomeMeta+: A Mixed-Prototyping Meta-Learning System Supporting AI Application Design AnywhereCode1
Master: Meta Style Transformer for Controllable Zero-Shot and Few-Shot Artistic Style Transfer0
Meta-tuning Loss Functions and Data Augmentation for Few-shot Object Detection0
Constructing a meta-learner for unsupervised anomaly detection0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8Unverified
2MZMeta-train success rate97.6Unverified
3MAMLMeta-test success rate36Unverified
4RL^2Meta-test success rate10Unverified
5DnCMeta-test success rate5.4Unverified
6PEARLMeta-test success rate0Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60Unverified
2Multi-task multi-head SACAverage Success Rate35.85Unverified
3DisCorAverage Success Rate26Unverified
4NDPAverage Success Rate11Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5Unverified
2MZMeta-test success rate (zero-shot)17.7Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75Unverified