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 27012750 of 2964 papers

TitleStatusHype
Generalized Zero- and Few-Shot Learning via Aligned Variational AutoencodersCode0
Sequential Scenario-Specific Meta Learner for Online RecommendationCode0
Multi-task Learning for Cross-Lingual Sentiment AnalysisCode0
Simultaneous Perturbation Method for Multi-Task Weight Optimization in One-Shot Meta-LearningCode0
Generalized Zero- and Few-Shot Learning via Aligned Variational AutoencodersCode0
TeFF: Tracking-enhanced Forgetting-free Few-shot 3D LiDAR Semantic SegmentationCode0
Generalized Knowledge Distillation via Relationship MatchingCode0
Multi-level Relation Learning for Cross-domain Few-shot Hyperspectral Image ClassificationCode0
Feature Extractor Stacking for Cross-domain Few-shot LearningCode0
TEII: Think, Explain, Interact and Iterate with Large Language Models to Solve Cross-lingual Emotion DetectionCode0
Beyond Interpretability: The Gains of Feature Monosemanticity on Model RobustnessCode0
Generalization Bounds For Meta-Learning: An Information-Theoretic AnalysisCode0
Contract Discovery: Dataset and a Few-Shot Semantic Retrieval Challenge with Competitive BaselinesCode0
GeneMask: Fast Pretraining of Gene Sequences to Enable Few-Shot LearningCode0
Mutually Guided Few-shot Learning for Relational Triple ExtractionCode0
Memorisation versus Generalisation in Pre-trained Language ModelsCode0
Gaussian Prototypical Networks for Few-Shot Learning on OmniglotCode0
MyriadAL: Active Few Shot Learning for HistopathologyCode0
Cross-Domain Few-Shot Learning via Adaptive Transformer NetworksCode0
Named Entity Recognition Under Domain Shift via Metric Learning for Life SciencesCode0
Gaussian Process Emulators for Few-Shot Segmentation in Cardiac MRICode0
Cross-Domain Cross-Set Few-Shot Learning via Learning Compact and Aligned RepresentationsCode0
Navigating Data Scarcity using Foundation Models: A Benchmark of Few-Shot and Zero-Shot Learning Approaches in Medical ImagingCode0
Navigating the Landscape of Large Language Models: A Comprehensive Review and Analysis of Paradigms and Fine-Tuning StrategiesCode0
Wave-SAN: Wavelet based Style Augmentation Network for Cross-Domain Few-Shot LearningCode0
Nearest Neighbour Few-Shot Learning for Cross-lingual ClassificationCode0
Near-real-time Earthquake-induced Fatality Estimation using Crowdsourced Data and Large-Language ModelsCode0
Fusion-based Few-Shot Morphing Attack Detection and FingerprintingCode0
Adaptive Cross-Modal Few-Shot LearningCode0
FsPONER: Few-shot Prompt Optimization for Named Entity Recognition in Domain-specific ScenariosCode0
Tensor feature hallucination for few-shot learningCode0
FSL-Rectifier: Rectify Outliers in Few-Shot Learning via Test-Time AugmentationCode0
CrisisMatch: Semi-Supervised Few-Shot Learning for Fine-Grained Disaster Tweet ClassificationCode0
tSF: Transformer-based Semantic Filter for Few-Shot LearningCode0
CPL-NoViD: Context-Aware Prompt-based Learning for Norm Violation Detection in Online CommunitiesCode0
Frequency-Guided Masking for Enhanced Vision Self-Supervised LearningCode0
Neural Data Augmentation via Example ExtrapolationCode0
Frankenstein Optimizer: Harnessing the Potential by Revisiting Optimization TricksCode0
Benchmarking Spurious Bias in Few-Shot Image ClassifiersCode0
A Neural-Symbolic Architecture for Inverse Graphics Improved by Lifelong Meta-LearningCode0
Flight of the PEGASUS? Comparing Transformers on Few-shot and Zero-shot Multi-document Abstractive SummarizationCode0
Neural Similarity LearningCode0
Neural Stored-program MemoryCode0
Neural Task Programming: Learning to Generalize Across Hierarchical TasksCode0
Neural TMDlayer: Modeling Instantaneous flow of features via SDE GeneratorsCode0
Selective Vision-Language Subspace Projection for Few-shot CLIPCode0
FLEURS: Few-shot Learning Evaluation of Universal Representations of SpeechCode0
Corrective In-Context Learning: Evaluating Self-Correction in Large Language ModelsCode0
Neurocache: Efficient Vector Retrieval for Long-range Language ModelingCode0
NeuroCLIP: Neuromorphic Data Understanding by CLIP and SNNCode0
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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