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 901950 of 2964 papers

TitleStatusHype
Learning to Find Common Objects Across Few Image CollectionsCode0
Learning to Learn Kernels with Variational Random FeaturesCode0
DRESS: Disentangled Representation-based Self-Supervised Meta-Learning for Diverse TasksCode0
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
Exploring the Similarity of Representations in Model-Agnostic Meta-LearningCode0
Learning New Tasks from a Few Examples with Soft-Label PrototypesCode0
Boosting keyword spotting through on-device learnable user speech characteristicsCode0
Extensively Matching for Few-shot Learning Event DetectionCode0
Nearest Neighbour Few-Shot Learning for Cross-lingual ClassificationCode0
Do Prompt-Based Models Really Understand the Meaning of their Prompts?Code0
Unlocking the Full Potential of Small Data with Diverse SupervisionCode0
Towards Context-Agnostic Learning Using Synthetic DataCode0
An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the WildCode0
Are Large Language Models Robust Coreference Resolvers?Code0
E-Sort: Empowering End-to-end Neural Network for Multi-channel Spike Sorting with Transfer Learning and Fast Post-processingCode0
Learning from the Tangram to Solve Mini Visual TasksCode0
Bayesian Active Meta-Learning for Few Pilot Demodulation and EqualizationCode0
Boosting Few-Shot Visual Learning with Self-SupervisionCode0
Learning Deep Disentangled Embeddings with the F-Statistic LossCode0
LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot LearningCode0
Learning Adaptive Classifiers Synthesis for Generalized Few-Shot LearningCode0
LCNN: Lookup-based Convolutional Neural NetworkCode0
LC-Protonets: Multi-Label Few-Shot Learning for World Music Audio TaggingCode0
learn2learn: A Library for Meta-Learning ResearchCode0
NeuroCLIP: Neuromorphic Data Understanding by CLIP and SNNCode0
Does MAML Only Work via Feature Re-use? A Data Centric PerspectiveCode0
Does language help generalization in vision models?Code0
An Efficient and Explainable Transformer-Based Few-Shot Learning for Modeling Electricity Consumption Profiles Across Thousands of DomainsCode0
Layer-Wise Feature Metric of Semantic-Pixel Matching for Few-Shot LearningCode0
LaSO: Label-Set Operations networks for multi-label few-shot learningCode0
DocLangID: Improving Few-Shot Training to Identify the Language of Historical DocumentsCode0
A Baseline for Few-Shot Image ClassificationCode0
Federated Few-shot Learning for Cough Classification with Edge DevicesCode0
LAVA: Label-efficient Visual Learning and AdaptationCode0
An Ensemble of Epoch-wise Empirical Bayes for Few-shot LearningCode0
Diversity with Cooperation: Ensemble Methods for Few-Shot ClassificationCode0
Diversity Transfer Network for Few-Shot LearningCode0
Large Language Models as Attribution Regularizers for Efficient Model TrainingCode0
Diverse Retrieval-Augmented In-Context Learning for Dialogue State TrackingCode0
Diverse Few-Shot Text Classification with Multiple MetricsCode0
BioRAGent: A Retrieval-Augmented Generation System for Showcasing Generative Query Expansion and Domain-Specific Search for Scientific Q&ACode0
Large Language Models Vote: Prompting for Rare Disease IdentificationCode0
Large Language Models are biased to overestimate profoundnessCode0
Acquiring Bidirectionality via Large and Small Language ModelsCode0
Large-Scale Few-Shot Learning: Knowledge Transfer With Class HierarchyCode0
A Named Entity Recognition Corpus for Vietnamese Biomedical Texts to Support Tuberculosis TreatmentCode0
Bi-Matching Mechanism to Combat the Long Tail of Word Sense DisambiguationCode0
Few-Max: Few-Shot Domain Adaptation for Unsupervised Contrastive Representation LearningCode0
Kernel Relative-prototype Spectral Filtering for Few-shot LearningCode0
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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