SOTAVerified

Few-Shot Learning

Few-Shot Learning is an example of meta-learning, where a learner is trained on several related tasks, during the meta-training phase, so that it can generalize well to unseen (but related) tasks with just few examples, during the meta-testing phase. An effective approach to the Few-Shot Learning problem is to learn a common representation for various tasks and train task specific classifiers on top of this representation.

Source: Penalty Method for Inversion-Free Deep Bilevel Optimization

Papers

Showing 851900 of 2964 papers

TitleStatusHype
Efficient Transfer Learning for Video-language Foundation ModelsCode0
Are Few-Shot Learning Benchmarks too Simple ? Solving them without Task Supervision at Test-TimeCode0
Two Sides of Meta-Learning Evaluation: In vs. Out of DistributionCode0
Leveraging Bottom-Up and Top-Down Attention for Few-Shot Object DetectionCode0
Learning to Propagate Labels: Transductive Propagation Network for Few-shot LearningCode0
Active Few-Shot Learning with FASLCode0
Learning to Learn Variational Semantic MemoryCode0
Bayesian Active Meta-Learning for Few Pilot Demodulation and EqualizationCode0
Learning to Propagate for Graph Meta-LearningCode0
Learning to learn via Self-CritiqueCode0
Learning to Learn By Self-CritiqueCode0
Meta Fine-Tuning Neural Language Models for Multi-Domain Text MiningCode0
BRUNO: A Deep Recurrent Model for Exchangeable DataCode0
Effectiveness of Cross-linguistic Extraction of Genetic Information using Generative Large Language ModelsCode0
Bringing Masked Autoencoders Explicit Contrastive Properties for Point Cloud Self-Supervised LearningCode0
Meta-learning for Classifying Previously Unseen Data Source into Previously Unseen Emotional CategoriesCode0
Learning to Find Common Objects Across Few Image CollectionsCode0
A New Dataset and Empirical Study for Sentence Simplification in ChineseCode0
Advancing Image Retrieval with Few-Shot Learning and Relevance FeedbackCode0
Learning to Forget for Meta-LearningCode0
Learning to Learn Kernels with Variational Random FeaturesCode0
Edge-labeling Graph Neural Network for Few-shot LearningCode0
Learning Prototype Representations Across Few-Shot Tasks for Event DetectionCode0
Edge-Labeling based Directed Gated Graph Network for Few-shot LearningCode0
Learning New Tasks from a Few Examples with Soft-Label PrototypesCode0
Dynamic Semantic Matching and Aggregation Network for Few-shot Intent DetectionCode0
Unlocking the Full Potential of Small Data with Diverse SupervisionCode0
Exploiting Causality Signals in Medical Images: A Pilot Study with Empirical ResultsCode0
A Neural Topic-Attention Model for Medical Term Abbreviation DisambiguationCode0
Bootstrapped Meta-LearningCode0
Learning from the Tangram to Solve Mini Visual TasksCode0
A Neural-Symbolic Architecture for Inverse Graphics Improved by Lifelong Meta-LearningCode0
ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and ClassificationCode0
Towards Context-Agnostic Learning Using Synthetic DataCode0
Learning to Few-Shot Learn Across Diverse Natural Language Classification TasksCode0
Class-Agnostic CountingCode0
One Representative-Shot Learning Using a Population-Driven Template with Application to Brain Connectivity Classification and Evolution PredictionCode0
Boosting the Generalization Capability in Cross-Domain Few-shot Learning via Noise-enhanced Supervised AutoencoderCode0
Dual-level Mixup for Graph Few-shot Learning with Fewer TasksCode0
LC-Protonets: Multi-Label Few-Shot Learning for World Music Audio TaggingCode0
An Ensemble of Epoch-wise Empirical Bayes for Few-shot LearningCode0
LCNN: Lookup-based Convolutional Neural NetworkCode0
LAVA: Label-efficient Visual Learning and AdaptationCode0
LaSO: Label-Set Operations networks for multi-label few-shot learningCode0
Layer-Wise Feature Metric of Semantic-Pixel Matching for Few-Shot LearningCode0
learn2learn: A Library for Meta-Learning ResearchCode0
DRESS: Disentangled Representation-based Self-Supervised Meta-Learning for Diverse TasksCode0
Large Language Models Vote: Prompting for Rare Disease IdentificationCode0
Do we still need Human Annotators? Prompting Large Language Models for Aspect Sentiment Quad PredictionCode0
Do Tutors Learn from Equity Training and Can Generative AI Assess It?Code0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1gpt-4-0125-previewAccuracy61.91Unverified
2gpt-4-0125-previewAccuracy52.49Unverified
3gpt-3.5-turboAccuracy41.48Unverified
4gpt-3.5-turboAccuracy37.06Unverified
5johnsnowlabs/JSL-MedMNX-7BAccuracy25.63Unverified
6yikuan8/Clinical-LongformerAccuracy25.55Unverified
7BioMistral/BioMistral-7B-DAREAccuracy25.06Unverified
8yikuan8/Clinical-LongformerAccuracy25.04Unverified
9PharMolix/BioMedGPT-LM-7BAccuracy24.92Unverified
10PharMolix/BioMedGPT-LM-7BAccuracy24.75Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean67.27Unverified
2SaSPA + CAL4-shot Accuracy48.3Unverified
3Real-Guidance + CAL4-shot Accuracy41.5Unverified
4CAL4-shot Accuracy40.9Unverified
#ModelMetricClaimedVerifiedStatus
1SaSPA + CALHarmonic mean52.2Unverified
2CALHarmonic mean35.2Unverified
3Variational Prompt TuningHarmonic mean34.69Unverified
4Real-Guidance + CALHarmonic mean34.5Unverified
#ModelMetricClaimedVerifiedStatus
1BGNNAccuracy92.7Unverified
2TIM-GDAccuracy87.4Unverified
3UNEM-GaussianAccuracy66.4Unverified
#ModelMetricClaimedVerifiedStatus
1EASY (transductive)Accuracy82.75Unverified
2HCTransformers5 way 1~2 shot74.74Unverified
3HyperShotAccuracy53.18Unverified
#ModelMetricClaimedVerifiedStatus
1SaSPA + CAL4-shot Accuracy66.7Unverified
2Real-Guidance + CAL4-shot Accuracy44.3Unverified
3CAL4-shot Accuracy42.2Unverified
#ModelMetricClaimedVerifiedStatus
1HCTransformersAcc74.74Unverified
2DPGNAcc67.6Unverified
#ModelMetricClaimedVerifiedStatus
1MetaGen Blended RAG (zero-shot)Accuracy77.9Unverified
2CoT-T5-11B (1024 Shot)Accuracy73.42Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean96.44Unverified
#ModelMetricClaimedVerifiedStatus
1CoT-T5-11B (1024 Shot)Accuracy68.3Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean77.71Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean81.12Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean91.57Unverified
#ModelMetricClaimedVerifiedStatus
1CovidExpertAUC-ROC1Unverified
#ModelMetricClaimedVerifiedStatus
1CoT-T5-11B (1024 Shot)Accuracy78.02Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy65.7Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy73.2Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean96.82Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean73.07Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean78.51Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy52.3Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean79Unverified