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 9511000 of 2964 papers

TitleStatusHype
When hard negative sampling meets supervised contrastive learning0
LongBench: A Bilingual, Multitask Benchmark for Long Context UnderstandingCode3
Fair Few-shot Learning with Auxiliary Sets0
VesselShot: Few-shot learning for cerebral blood vessel segmentation0
Transfer Learning for Microstructure Segmentation with CS-UNet: A Hybrid Algorithm with Transformer and CNN EncodersCode1
Compressor-Based Classification for Atrial Fibrillation Detection0
Large Language Models Vote: Prompting for Rare Disease IdentificationCode0
Diagnosing Infeasible Optimization Problems Using Large Language ModelsCode1
Cabrita: closing the gap for foreign languages0
Few-shot Anomaly Detection in Text with Deviation Learning0
Measuring the Effect of Causal Disentanglement on the Adversarial Robustness of Neural Network Models0
Refashioning Emotion Recognition Modelling: The Advent of Generalised Large Models0
COCA: Classifier-Oriented Calibration via Textual Prototype for Source-Free Universal Domain AdaptationCode0
UniAP: Towards Universal Animal Perception in Vision via Few-shot Learning0
BioMedGPT: Open Multimodal Generative Pre-trained Transformer for BioMedicine0
CodeCoT: Tackling Code Syntax Errors in CoT Reasoning for Code Generation0
Knowledge-Enhanced Multi-Label Few-Shot Product Attribute-Value ExtractionCode0
Link-Context Learning for Multimodal LLMsCode1
Dialogue for Prompting: a Policy-Gradient-Based Discrete Prompt Generation for Few-shot LearningCode1
Approximating Human-Like Few-shot Learning with GPT-based Compression0
Few-shot Class-incremental Learning for Classification and Object Detection: A Survey0
Demonstration-based learning for few-shot biomedical named entity recognition under machine reading comprehensionCode0
AutoConv: Automatically Generating Information-seeking Conversations with Large Language Models0
Complex Facial Expression Recognition Using Deep Knowledge Distillation of Basic FeaturesCode0
Few-shot pixel-precise document layout segmentation via dynamic instance generation and local thresholding0
Cross-heterogeneity Graph Few-shot Learning0
Global in Local: A Convolutional Transformer for SAR ATR FSL0
LLMeBench: A Flexible Framework for Accelerating LLMs BenchmarkingCode1
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
DiffKendall: A Novel Approach for Few-Shot Learning with Differentiable Kendall's Rank CorrelationCode0
Cross-Modal Concept Learning and Inference for Vision-Language Models0
Distilled Feature Fields Enable Few-Shot Language-Guided ManipulationCode2
Learning Multi-modal Representations by Watching Hundreds of Surgical Video LecturesCode1
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
Self-supervised Few-shot Learning for Semantic Segmentation: An Annotation-free ApproachCode1
Sparse annotation strategies for segmentation of short axis cardiac MRI0
Wisdom of Instruction-Tuned Language Model Crowds. Exploring Model Label Variation0
Identifying Misinformation on YouTube through Transcript Contextual Analysis with Transformer ModelsCode0
CohortGPT: An Enhanced GPT for Participant Recruitment in Clinical Study0
DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AICode2
Mutual Reinforcement Effects in Japanese Sentence Classification and Named Entity Recognition Tasks0
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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