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 2301–2325 of 3569 papers

TitleStatusHype
Towards Intelligent Pick and Place Assembly of Individualized Products Using Reinforcement Learning—0
Towards intervention-centric causal reasoning in learning agents—0
Towards Learning to Remember in Meta Learning of Sequential Domains—0
Towards Low-Resource Semi-Supervised Dialogue Generation with Meta-Learning—0
Towards meta-learning for multi-target regression problems—0
Towards more efficient agricultural practices via transformer-based crop type classification—0
Towards Multi-Domain Single Image Dehazing via Test-Time Training—0
Towards Reliable Neural Machine Translation with Consistency-Aware Meta-Learning—0
Towards Robust and Interpretable EMG-based Hand Gesture Recognition using Deep Metric Meta Learning—0
Towards Robust Graph Neural Networks against Label Noise—0
Towards Robust Physical-world Backdoor Attacks on Lane Detection—0
Towards robust prediction of material properties for nuclear reactor design under scarce data -- a study in creep rupture property—0
Towards Scalable and Robust Structured Bandits: A Meta-Learning Framework—0
Towards Sharper Information-theoretic Generalization Bounds for Meta-Learning—0
Towards Sleep Scoring Generalization Through Self-Supervised Meta-Learning—0
Towards Subject Agnostic Affective Emotion Recognition—0
Towards Tailored Models on Private AIoT Devices: Federated Direct Neural Architecture Search—0
Towards Understanding Generalization in Gradient-Based Meta-Learning—0
Towards Unified Task Embeddings Across Multiple Models: Bridging the Gap for Prompt-Based Large Language Models and Beyond—0
Hyperparameter Optimization for Unsupervised Outlier Detection—0
Towards Zero-Shot Learning with Fewer Seen Class Examples—0
Tracking by Instance Detection: A Meta-Learning Approach—0
Training an Interactive Helper—0
Training Data Generating Networks: Shape Reconstruction via Bi-level Optimization—0
Training few-shot classification via the perspective of minibatch and pretraining—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