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 251–300 of 2964 papers

TitleStatusHype
A Novel Compact LLM Framework for Local, High-Privacy EHR Data Applications—0
Does Few-Shot Learning Help LLM Performance in Code Synthesis?—0
Concept Replacer: Replacing Sensitive Concepts in Diffusion Models via Precision LocalizationCode0
Unleashing In-context Learning of Autoregressive Models for Few-shot Image Manipulation—0
Towards Cross-Lingual Audio Abuse Detection in Low-Resource Settings with Few-Shot LearningCode0
Prompt as Free Lunch: Enhancing Diversity in Source-Free Cross-domain Few-shot Learning through Semantic-Guided Prompting—0
Enhancing AI microscopy for foodborne bacterial classification via adversarial domain adaptation across optical and biological variability—0
APT: Architectural Planning and Text-to-Blueprint Construction Using Large Language Models for Open-World AgentsCode0
SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting—0
ASSERTIFY: Utilizing Large Language Models to Generate Assertions for Production CodeCode0
Enhancing Few-Shot Learning with Integrated Data and GAN Model Approaches—0
Low-Data Classification of Historical Music Manuscripts: A Few-Shot Learning Approach—0
Beyond Data Scarcity: A Frequency-Driven Framework for Zero-Shot Forecasting—0
Comparative Analysis of Resource-Efficient CNN Architectures for Brain Tumor Classification—0
Point Cloud Understanding via Attention-Driven Contrastive Learning—0
FOCUS: Knowledge-enhanced Adaptive Visual Compression for Few-shot Whole Slide Image ClassificationCode1
Exploring Foundation Models Fine-Tuning for Cytology ClassificationCode1
AdaptAgent: Adapting Multimodal Web Agents with Few-Shot Learning from Human Demonstrations—0
Prototype Optimization with Neural ODE for Few-Shot Learning—0
Efficient Transfer Learning for Video-language Foundation ModelsCode0
Hyperspectral Imaging-Based Grain Quality Assessment With Limited Labelled Data—0
Step-wise Distribution Alignment Guided Style Prompt Tuning for Source-free Cross-domain Few-shot LearningCode1
A Practical Guide to Fine-tuning Language Models with Limited Data—0
Embedding Space Allocation with Angle-Norm Joint Classifiers for Few-Shot Class-Incremental Learning—0
Assessing the Performance of the DINOv2 Self-supervised Learning Vision Transformer Model for the Segmentation of the Left Atrium from MRI Images—0
Towards Evaluating Large Language Models for Graph Query Generation—0
GPTree: Towards Explainable Decision-Making via LLM-powered Decision Trees—0
CoCoP: Enhancing Text Classification with LLM through Code Completion Prompt—0
The Surprising Effectiveness of Test-Time Training for Few-Shot LearningCode3
Gaussian Process Emulators for Few-Shot Segmentation in Cardiac MRICode0
Layer-Wise Feature Metric of Semantic-Pixel Matching for Few-Shot LearningCode0
Clustering Algorithms and RAG Enhancing Semi-Supervised Text Classification with Large LLMs—0
Selecting Between BERT and GPT for Text Classification in Political Science Research—0
Gradient Boosting Trees and Large Language Models for Tabular Data Few-Shot Learning—0
A Contrastive Self-Supervised Learning scheme for beat tracking amenable to few-shot learning—0
Quantum Diffusion Models for Few-Shot Learning—0
Leveraging Vision-Language Models for Manufacturing Feature Recognition in CAD Designs—0
Graph-DPEP: Decomposed Plug and Ensemble Play for Few-Shot Document Relation Extraction with Graph-of-Thoughts Reasoning—0
Meta-Exploiting Frequency Prior for Cross-Domain Few-Shot Learning—0
Fast Adaptation with Kernel and Gradient based Meta Leaning—0
Emoji Attack: A Method for Misleading Judge LLMs in Safety Risk DetectionCode1
Large Language Models for Patient Comments Multi-Label Classification—0
FewVS: A Vision-Semantics Integration Framework for Few-Shot Image ClassificationCode1
LLM-Forest: Ensemble Learning of LLMs with Graph-Augmented Prompts for Data Imputation—0
Contextual Representation Anchor Network to Alleviate Selection Bias in Few-Shot Drug Discovery—0
Video to Video Generative Adversarial Network for Few-shot Learning Based on Policy Gradient—0
Shopping MMLU: A Massive Multi-Task Online Shopping Benchmark for Large Language ModelsCode1
Beyond Interpretability: The Gains of Feature Monosemanticity on Model RobustnessCode0
Prompt Diffusion Robustifies Any-Modality Prompt Learning—0
Vulnerability of LLMs to Vertically Aligned Text Manipulations—0
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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