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 551600 of 2964 papers

TitleStatusHype
How Reliable AI Chatbots are for Disease Prediction from Patient Complaints?0
Investigating Persuasion Techniques in Arabic: An Empirical Study Leveraging Large Language Models0
Mining the Explainability and Generalization: Fact Verification Based on Self-Instruction0
Like Humans to Few-Shot Learning through Knowledge Permeation of Vision and Text0
Customize Your Own Paired Data via Few-shot Way0
Perturbing the Gradient for Alleviating Meta OverfittingCode0
WisPerMed at BioLaySumm: Adapting Autoregressive Large Language Models for Lay Summarization of Scientific Articles0
Zero-Shot Stance Detection using Contextual Data Generation with LLMsCode0
NetMamba: Efficient Network Traffic Classification via Pre-training Unidirectional MambaCode2
WisPerMed at "Discharge Me!": Advancing Text Generation in Healthcare with Large Language Models, Dynamic Expert Selection, and Priming Techniques on MIMIC-IV0
ActiveLLM: Large Language Model-based Active Learning for Textual Few-Shot Scenarios0
A Versatile Framework for Analyzing Galaxy Image Data by Implanting Human-in-the-loop on a Large Vision Model0
Support-Query Prototype Fusion Network for Few-shot Medical Image Segmentation0
Dehazing Remote Sensing and UAV Imagery: A Review of Deep Learning, Prior-based, and Hybrid Approaches0
MedConceptsQA: Open Source Medical Concepts QA BenchmarkCode1
Execution-Based Evaluation of Natural Language to Bash and PowerShell for Incident RemediationCode0
CANAL -- Cyber Activity News Alerting Language Model: Empirical Approach vs. Expensive LLM0
Iris: An AI-Driven Virtual Tutor For Computer Science Education0
Reddit-Impacts: A Named Entity Recognition Dataset for Analyzing Clinical and Social Effects of Substance Use Derived from Social Media0
A Mixture-of-Experts Approach to Few-Shot Task Transfer in Open-Ended Text Worlds0
Detecting Statements in Text: A Domain-Agnostic Few-Shot SolutionCode0
Continuous max-flow augmentation of self-supervised few-shot learning on SPECT left ventriclesCode0
EVA-X: A Foundation Model for General Chest X-ray Analysis with Self-supervised LearningCode0
DrugLLM: Open Large Language Model for Few-shot Molecule Generation0
Intra-task Mutual Attention based Vision Transformer for Few-Shot Learning0
Multi-Agent RL-Based Industrial AIGC Service Offloading over Wireless Edge Networks0
Open-SQL Framework: Enhancing Text-to-SQL on Open-source Large Language Models0
Enhancing News Summarization with ELearnFit through Efficient In-Context Learning and Efficient Fine-Tuning0
A Survey of Time Series Foundation Models: Generalizing Time Series Representation with Large Language ModelCode2
A Survey of Few-Shot Learning for Biomedical Time Series0
Accelerating Convergence in Bayesian Few-Shot ClassificationCode0
UniGen: Universal Domain Generalization for Sentiment Classification via Zero-shot Dataset GenerationCode0
A separability-based approach to quantifying generalization: which layer is best?Code0
Variational Neuron Shifting for Few-Shot Image Classification Across Domains0
Exploring Self-Supervised Vision Transformers for Deepfake Detection: A Comparative AnalysisCode0
PEVA-Net: Prompt-Enhanced View Aggregation Network for Zero/Few-Shot Multi-View 3D Shape Recognition0
StablePT: Towards Stable Prompting for Few-shot Learning via Input SeparationCode0
PEFSL: A deployment Pipeline for Embedded Few-Shot Learning on a FPGA SoC0
UniFS: Universal Few-shot Instance Perception with Point RepresentationsCode1
Ta-Adapter: Enhancing few-shot CLIP with task-aware encoders0
Certification of Speaker Recognition Models to Additive PerturbationsCode0
Evaluation of Few-Shot Learning for Classification Tasks in the Polish Language0
Meta In-Context Learning Makes Large Language Models Better Zero and Few-Shot Relation Extractors0
Empowering Large Language Models for Textual Data Augmentation0
Meta-Transfer Derm-Diagnosis: Exploring Few-Shot Learning and Transfer Learning for Skin Disease Classification in Long-Tail Distribution0
AAPL: Adding Attributes to Prompt Learning for Vision-Language ModelsCode1
Beyond Deepfake Images: Detecting AI-Generated Videos0
A comprehensive and easy-to-use multi-domain multi-task medical imaging meta-dataset (MedIMeta)0
Identifying Fairness Issues in Automatically Generated Testing Content0
Graph Machine Learning in the Era of Large Language Models (LLMs)0
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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