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 2126–2150 of 3569 papers

TitleStatusHype
Meta-Learned Feature Critics for Domain Generalized Semantic Segmentation—0
MetaCVR: Conversion Rate Prediction via Meta Learning in Small-Scale Recommendation Scenarios—0
Does CLIP Benefit Visual Question Answering in the Medical Domain as Much as it Does in the General Domain?—0
Does MAML Only Work via Feature Re-use? A Data Centric PerspectiveCode0
Dynamic Channel Access via Meta-Reinforcement Learning—0
The Curse of Zero Task Diversity: On the Failure of Transfer Learning to Outperform MAML and their Empirical Equivalence—0
Deep Neuroevolution Squeezes More out of Small Neural Networks and Small Training Sets: Sample Application to MRI Brain Sequence Classification—0
MVDG: A Unified Multi-view Framework for Domain GeneralizationCode0
Dual Path Structural Contrastive Embeddings for Learning Novel Objects—0
Meta-Learning and Self-Supervised Pretraining for Real World Image TranslationCode0
Generalized Few-Shot Semantic Segmentation: All You Need is Fine-Tuning—0
Improving Unsupervised Stain-To-Stain Translation using Self-Supervision and Meta-Learning—0
Integrated Guidance and Control for Lunar Landing using a Stabilized Seeker—0
Learning to acquire novel cognitive tasks with evolution, plasticity and meta-meta-learningCode0
Automatic tuning of hyper-parameters of reinforcement learning algorithms using Bayesian optimization with behavioral cloning—0
AllWOZ: Towards Multilingual Task-Oriented Dialog Systems for All—0
Improving Both Domain Robustness and Domain Adaptability in Machine TranslationCode0
How to Learn and Represent Abstractions: An Investigation using Symbolic AlchemyCode0
Leaping Through Time with Gradient-based Adaptation for RecommendationCode0
Learning to Learn Transferable AttackCode0
PMFL: Partial Meta-Federated Learning for heterogeneous tasks and its applications on real-world medical recordsCode0
CoMPS: Continual Meta Policy Search—0
MESA: Offline Meta-RL for Safe Adaptation and Fault Tolerance—0
Curriculum Meta-Learning for Few-shot ClassificationCode0
MetaCloth: Learning Unseen Tasks of Dense Fashion Landmark Detection from a Few Samples—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