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 15011550 of 2964 papers

TitleStatusHype
Knowledge-Enhanced Multi-Label Few-Shot Product Attribute-Value ExtractionCode0
Approximating Human-Like Few-shot Learning with GPT-based Compression0
Few-shot Class-incremental Learning for Classification and Object Detection: A Survey0
AutoConv: Automatically Generating Information-seeking Conversations with Large Language Models0
Demonstration-based learning for few-shot biomedical named entity recognition under machine reading comprehensionCode0
Complex Facial Expression Recognition Using Deep Knowledge Distillation of Basic FeaturesCode0
Cross-heterogeneity Graph Few-shot Learning0
Few-shot pixel-precise document layout segmentation via dynamic instance generation and local thresholding0
Global in Local: A Convolutional Transformer for SAR ATR FSL0
ChatGPT for Arabic Grammatical Error Correction0
Prototypes-oriented Transductive Few-shot Learning with Conditional Transport0
Meta-learning in healthcare: A survey0
Does Correction Remain A Problem For Large Language Models?0
Thespian: Multi-Character Text Role-Playing Game Agents0
Leveraging Few-Shot Data Augmentation and Waterfall Prompting for Response Generation0
MetaDiff: Meta-Learning with Conditional Diffusion for Few-Shot Learning0
GeneMask: Fast Pretraining of Gene Sequences to Enable Few-Shot LearningCode0
Cross-Modal Concept Learning and Inference for Vision-Language Models0
DiffKendall: A Novel Approach for Few-Shot Learning with Differentiable Kendall's Rank CorrelationCode0
GenCo: An Auxiliary Generator from Contrastive Learning for Enhanced Few-Shot Learning in Remote Sensing0
Exploiting the Potential of Seq2Seq Models as Robust Few-Shot Learners0
A Weakly Supervised Segmentation Network Embedding Cross-scale Attention Guidance and Noise-sensitive Constraint for Detecting Tertiary Lymphoid Structures of Pancreatic Tumors0
Wisdom of Instruction-Tuned Language Model Crowds. Exploring Model Label Variation0
Sparse annotation strategies for segmentation of short axis cardiac MRI0
Identifying Misinformation on YouTube through Transcript Contextual Analysis with Transformer ModelsCode0
CohortGPT: An Enhanced GPT for Participant Recruitment in Clinical Study0
Mutual Reinforcement Effects in Japanese Sentence Classification and Named Entity Recognition Tasks0
A metric learning approach for endoscopic kidney stone identification0
TinyMetaFed: Efficient Federated Meta-Learning for TinyML0
SAR-NeRF: Neural Radiance Fields for Synthetic Aperture Radar Multi-View Representation0
Feature Activation Map: Visual Explanation of Deep Learning Models for Image Classification0
Text Descriptions are Compressive and Invariant Representations for Visual Learning0
FILM: How can Few-Shot Image Classification Benefit from Pre-Trained Language Models?0
Evaluating the Evaluators: Are Current Few-Shot Learning Benchmarks Fit for Purpose?0
Task-Specific Alignment and Multiple Level Transformer for Few-Shot Action RecognitionCode0
TablEye: Seeing small Tables through the Lens of Images0
Optimal and Efficient Binary Questioning for Human-in-the-Loop Annotation0
Diverse Retrieval-Augmented In-Context Learning for Dialogue State TrackingCode0
On Conditional and Compositional Language Model Differentiable Prompting0
Meta-training with Demonstration Retrieval for Efficient Few-shot Learning0
Benchmarking Large Language Model Capabilities for Conditional Generation0
Effective Transfer of Pretrained Large Visual Model for Fabric Defect Segmentation via Specifc Knowledge Injection0
Language models are weak learners0
Is Pre-training Truly Better Than Meta-Learning?0
Mutually Guided Few-shot Learning for Relational Triple ExtractionCode0
FlakyFix: Using Large Language Models for Predicting Flaky Test Fix Categories and Test Code Repair0
NeuroCLIP: Neuromorphic Data Understanding by CLIP and SNNCode0
Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Prompts0
Visually grounded few-shot word learning in low-resource settings0
Multilingual Few-Shot Learning via Language Model Retrieval0
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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