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 1801–1825 of 3569 papers

TitleStatusHype
Camera Distortion-aware 3D Human Pose Estimation in Video with Optimization-based Meta-LearningCode1
MAMRL: Exploiting Multi-agent Meta Reinforcement Learning in WAN Traffic Engineering—0
Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label MiscorrectionCode1
Confounder Identification-free Causal Visual Feature Learning—0
Joint inference and input optimization in equilibrium networksCode0
A Close Look at Few-shot Real Image Super-resolution from the Distortion Relation Perspective—0
VaxNeRF: Revisiting the Classic for Voxel-Accelerated Neural Radiance FieldCode1
Meta Mask Correction for Nuclei Segmentation in Histopathological Image—0
Exploration of Dark Chemical Genomics Space via Portal Learning: Applied to Targeting the Undruggable Genome and COVID-19 Anti-Infective Polypharmacology—0
Sharing to learn and learning to share; Fitting together Meta-Learning, Multi-Task Learning, and Transfer Learning: A meta review—0
Meta-learning richer priors for VAEs—0
Towards Improving Embedding Based Models of Social Network Alignment via Pseudo AnchorsCode1
Reinforcement Learning for Few-Shot Text Generation AdaptationCode0
Generating meta-learning tasks to evolve parametric loss for classification learning—0
Visual Goal-Directed Meta-Learning with Contextual Planning Networks—0
Meta Learning for Code Summarization—0
A Meta-Learning Approach for Few-Shot (Dis)Agreement Identification in Online Discussions—0
MetaPrompting: Learning to Learn Better Prompts—0
ST-SQL: Semi-Supervised Self-Training for Text-to-SQL via Column Specificity Meta-LearningCode0
Meta-learning via Language Model In-context Tuning—0
DAML-ST5: Low Resource Style Transfer via Domain Adaptive Meta Learning—0
Unsupervised Domain Adaptation for Event Detection via Meta Self-Paced Learning—0
DAML: Chinese Named Entity Recognition with a fusion method of data-augmentation and meta-learning—0
UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis—0
Persian Natural Language Inference: A Meta-learning approach—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