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 17511775 of 3569 papers

TitleStatusHype
Does CLIP Benefit Visual Question Answering in the Medical Domain as Much as it Does in the General Domain?0
Meta-Learned Feature Critics for Domain Generalized Semantic Segmentation0
MetaCVR: Conversion Rate Prediction via Meta Learning in Small-Scale Recommendation Scenarios0
Few-Shot Classification in Unseen Domains by Episodic Meta-Learning Across Visual Domains0
N-Omniglot, a large-scale neuromorphic dataset for spatio-temporal sparse few-shot learningCode1
Dynamic Channel Access via Meta-Reinforcement Learning0
The Curse of Zero Task Diversity: On the Failure of Transfer Learning to Outperform MAML and their Empirical Equivalence0
Does MAML Only Work via Feature Re-use? A Data Centric PerspectiveCode0
Deep Neuroevolution Squeezes More out of Small Neural Networks and Small Training Sets: Sample Application to MRI Brain Sequence Classification0
Graph Few-shot Class-incremental LearningCode1
Dual Path Structural Contrastive Embeddings for Learning Novel Objects0
MVDG: A Unified Multi-view Framework for Domain GeneralizationCode0
Meta-Learning and Self-Supervised Pretraining for Real World Image TranslationCode0
Generalized Few-Shot Semantic Segmentation: All You Need is Fine-Tuning0
Meta Propagation Networks for Graph Few-shot Semi-supervised LearningCode1
Query Adaptive Few-Shot Object Detection with Heterogeneous Graph Convolutional NetworksCode1
Learning to acquire novel cognitive tasks with evolution, plasticity and meta-meta-learningCode0
Integrated Guidance and Control for Lunar Landing using a Stabilized Seeker0
Improving Unsupervised Stain-To-Stain Translation using Self-Supervision and Meta-Learning0
Automatic tuning of hyper-parameters of reinforcement learning algorithms using Bayesian optimization with behavioral cloning0
AllWOZ: Towards Multilingual Task-Oriented Dialog Systems for All0
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
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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