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

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Showing 1151–1200 of 2964 papers

TitleStatusHype
Self-Supervised and Few-Shot Learning for Robust Bioaerosol Monitoring—0
mOSCAR: A Large-scale Multilingual and Multimodal Document-level Corpus—0
Meta-Learning Neural Procedural Biases—0
Bilingual Sexism Classification: Fine-Tuned XLM-RoBERTa and GPT-3.5 Few-Shot Learning—0
Anomaly Multi-classification in Industrial Scenarios: Transferring Few-shot Learning to a New TaskCode0
Text2VP: Generative AI for Visual Programming and Parametric Modeling—0
Few-Shot Load Forecasting Under Data Scarcity in Smart Grids: A Meta-Learning Approach—0
MaTableGPT: GPT-based Table Data Extractor from Materials Science Literature—0
Low-Resource Cross-Lingual Summarization through Few-Shot Learning with Large Language Models—0
Graph Mining under Data scarcity—0
LLM-based Rewriting of Inappropriate Argumentation using Reinforcement Learning from Machine FeedbackCode0
Self-Supervised Skeleton-Based Action Representation Learning: A Benchmark and BeyondCode0
Enhancing Trust in LLMs: Algorithms for Comparing and Interpreting LLMs—0
Detecting Endangered Marine Species in Autonomous Underwater Vehicle Imagery Using Point Annotations and Few-Shot Learning—0
Strengthening Network Intrusion Detection in IoT Environments with Self-Supervised Learning and Few Shot Learning—0
Understanding the Cross-Domain Capabilities of Video-Based Few-Shot Action Recognition Models—0
Applying Fine-Tuned LLMs for Reducing Data Needs in Load Profile Analysis—0
Wav2Prompt: End-to-End Speech Prompt Generation and Tuning For LLM in Zero and Few-shot Learning—0
Large Language Models: A New Approach for Privacy Policy Analysis at Scale—0
Few-shot fault diagnosis based on multi-scale graph convolution filtering for industry—0
Multimodal Cross-Domain Few-Shot Learning for Egocentric Action Recognition—0
Learning Human-Aligned Representations with Contrastive Learning and Generative Similarity—0
On the Limits of Multi-modal Meta-Learning with Auxiliary Task Modulation Using Conditional Batch Normalization—0
Mashee at SemEval-2024 Task 8: The Impact of Samples Quality on the Performance of In-Context Learning for Machine Text Classification—0
RAGSys: Item-Cold-Start Recommender as RAG System—0
Exploring the LLM Journey from Cognition to Expression with Linear Representations—0
On Understanding Attention-Based In-Context Learning for Categorical Data—0
TEII: Think, Explain, Interact and Iterate with Large Language Models to Solve Cross-lingual Emotion DetectionCode0
Magnetic Resonance Image Processing Transformer for General Accelerated Image Reconstruction—0
What Makes Good Few-shot Examples for Vision-Language Models?—0
Investigating Persuasion Techniques in Arabic: An Empirical Study Leveraging Large Language Models—0
Like Humans to Few-Shot Learning through Knowledge Permeation of Vision and Text—0
Customize Your Own Paired Data via Few-shot Way—0
How Reliable AI Chatbots are for Disease Prediction from Patient Complaints?—0
Mining the Explainability and Generalization: Fact Verification Based on Self-Instruction—0
Perturbing the Gradient for Alleviating Meta OverfittingCode0
WisPerMed at BioLaySumm: Adapting Autoregressive Large Language Models for Lay Summarization of Scientific Articles—0
Zero-Shot Stance Detection using Contextual Data Generation with LLMsCode0
WisPerMed at "Discharge Me!": Advancing Text Generation in Healthcare with Large Language Models, Dynamic Expert Selection, and Priming Techniques on MIMIC-IV—0
ActiveLLM: Large Language Model-based Active Learning for Textual Few-Shot Scenarios—0
A Versatile Framework for Analyzing Galaxy Image Data by Implanting Human-in-the-loop on a Large Vision Model—0
Dehazing Remote Sensing and UAV Imagery: A Review of Deep Learning, Prior-based, and Hybrid Approaches—0
Support-Query Prototype Fusion Network for Few-shot Medical Image Segmentation—0
CANAL -- Cyber Activity News Alerting Language Model: Empirical Approach vs. Expensive LLM—0
Execution-Based Evaluation of Natural Language to Bash and PowerShell for Incident RemediationCode0
Reddit-Impacts: A Named Entity Recognition Dataset for Analyzing Clinical and Social Effects of Substance Use Derived from Social Media—0
Iris: An AI-Driven Virtual Tutor For Computer Science Education—0
Detecting Statements in Text: A Domain-Agnostic Few-Shot SolutionCode0
A Mixture-of-Experts Approach to Few-Shot Task Transfer in Open-Ended Text Worlds—0
Continuous max-flow augmentation of self-supervised few-shot learning on SPECT left ventriclesCode0
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All datasetsMedConceptsQADTDFGVC-AircraftMini-ImageNet - 5-Shot LearningMini-Imagenet 5-way (1-shot)Stanford CarsMini-ImageNet - 1-Shot LearningPubMedQACaltech101CaseHOLDEuroSATFlowers-102

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1gpt-4-0125-previewAccuracy61.91—Unverified
2gpt-4-0125-previewAccuracy52.49—Unverified
3gpt-3.5-turboAccuracy41.48—Unverified
4gpt-3.5-turboAccuracy37.06—Unverified
5johnsnowlabs/JSL-MedMNX-7BAccuracy25.63—Unverified
6yikuan8/Clinical-LongformerAccuracy25.55—Unverified
7BioMistral/BioMistral-7B-DAREAccuracy25.06—Unverified
8yikuan8/Clinical-LongformerAccuracy25.04—Unverified
9PharMolix/BioMedGPT-LM-7BAccuracy24.92—Unverified
10PharMolix/BioMedGPT-LM-7BAccuracy24.75—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean67.27—Unverified
2SaSPA + CAL4-shot Accuracy48.3—Unverified
3Real-Guidance + CAL4-shot Accuracy41.5—Unverified
4CAL4-shot Accuracy40.9—Unverified
#ModelMetricClaimedVerifiedStatus
1SaSPA + CALHarmonic mean52.2—Unverified
2CALHarmonic mean35.2—Unverified
3Variational Prompt TuningHarmonic mean34.69—Unverified
4Real-Guidance + CALHarmonic mean34.5—Unverified
#ModelMetricClaimedVerifiedStatus
1BGNNAccuracy92.7—Unverified
2TIM-GDAccuracy87.4—Unverified
3UNEM-GaussianAccuracy66.4—Unverified
#ModelMetricClaimedVerifiedStatus
1EASY (transductive)Accuracy82.75—Unverified
2HCTransformers5 way 1~2 shot74.74—Unverified
3HyperShotAccuracy53.18—Unverified
#ModelMetricClaimedVerifiedStatus
1SaSPA + CAL4-shot Accuracy66.7—Unverified
2Real-Guidance + CAL4-shot Accuracy44.3—Unverified
3CAL4-shot Accuracy42.2—Unverified
#ModelMetricClaimedVerifiedStatus
1HCTransformersAcc74.74—Unverified
2DPGNAcc67.6—Unverified
#ModelMetricClaimedVerifiedStatus
1MetaGen Blended RAG (zero-shot)Accuracy77.9—Unverified
2CoT-T5-11B (1024 Shot)Accuracy73.42—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean96.44—Unverified
#ModelMetricClaimedVerifiedStatus
1CoT-T5-11B (1024 Shot)Accuracy68.3—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean77.71—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean81.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean91.57—Unverified
#ModelMetricClaimedVerifiedStatus
1CovidExpertAUC-ROC1—Unverified
#ModelMetricClaimedVerifiedStatus
1CoT-T5-11B (1024 Shot)Accuracy78.02—Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy65.7—Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy73.2—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean96.82—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean73.07—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean78.51—Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy52.3—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean79—Unverified