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 17511800 of 2964 papers

TitleStatusHype
AgileNet: Lightweight Dictionary-based Few-shot Learning0
Malicious Requests Detection with Improved Bidirectional Long Short-term Memory Neural Networks0
A Minimalist Dataset for Systematic Generalization of Perception, Syntax, and Semantics0
A Holistic Evaluation of Piano Sound Quality0
A Hybrid Approach with Optimization and Metric-based Meta-Learner for Few-Shot Learning0
A Hybrid Model for Few-Shot Text Classification Using Transfer and Meta-Learning0
AI-Driven Road Maintenance Inspection v2: Reducing Data Dependency & Quantifying Road Damage0
AI for Climate Finance: Agentic Retrieval and Multi-Step Reasoning for Early Warning System Investments0
AimTS: Augmented Series and Image Contrastive Learning for Time Series Classification0
AI of Brain and Cognitive Sciences: From the Perspective of First Principles0
AirFi: Empowering WiFi-based Passive Human Gesture Recognition to Unseen Environment via Domain Generalization0
AISFG: Abundant Information Slot Filling Generator0
Towards Answering Open-ended Ethical Quandary Questions0
A Large Language Model for Feasible and Diverse Population Synthesis0
A Lazy Approach to Long-Horizon Gradient-Based Meta-Learning0
ALERT: Adapting Language Models to Reasoning Tasks0
Aligning MAGMA by Few-Shot Learning and Finetuning0
Aligning Visual Prototypes with BERT Embeddings for Few-Shot Learning0
Alignment with human representations supports robust few-shot learning0
A Little Leak Will Sink a Great Ship: Survey of Transparency for Large Language Models from Start to Finish0
All About Knowledge Graphs for Actions0
Alleviating the Incompatibility between Cross Entropy Loss and Episode Training for Few-shot Skin Disease Classification0
ALLSH: Active Learning Guided by Local Sensitivity and Hardness0
All You Need in Knowledge Distillation Is a Tailored Coordinate System0
Learning Multi-level Weight-centric Features for Few-shot Learning0
ALMA: Alignment with Minimal Annotation0
Alpha MAML: Adaptive Model-Agnostic Meta-Learning0
AltGDmin: Alternating GD and Minimization for Partly-Decoupled (Federated) Optimization0
ALWNN Empowered Automatic Modulation Classification: Conquering Complexity and Scarce Sample Conditions0
A metric learning approach for endoscopic kidney stone identification0
A MIMO Radar-based Few-Shot Learning Approach for Human-ID0
A MIMO Radar-Based Metric Learning Approach for Activity Recognition0
A Mixture-of-Experts Approach to Few-Shot Task Transfer in Open-Ended Text Worlds0
Amortized Bayesian Meta-Learning0
AMP0: Species-Specific Prediction of Anti-microbial Peptides using Zero and Few Shot Learning0
A Multi-solution Study on GDPR AI-enabled Completeness Checking of DPAs0
An Adaptive Plug-and-Play Network for Few-Shot Learning0
Analogy-Forming Transformers for Few-Shot 3D Parsing0
Analysis and Applications of Deep Learning with Finite Samples in Full Life-Cycle Intelligence of Nuclear Power Generation0
Analyzing and Adapting Large Language Models for Few-Shot Multilingual NLU: Are We There Yet?0
Identifying and Analyzing Task-Encoding Tokens in Large Language Models0
Analyzing Text Representations by Measuring Task Alignment0
An Analysis of LLM Fine-Tuning and Few-Shot Learning for Flaky Test Detection and Classification0
An Artificial Intelligence-Based System to Assess Nutrient Intake for Hospitalised Patients0
An Autonomous Network Orchestration Framework Integrating Large Language Models with Continual Reinforcement Learning0
Ancient Script Image Recognition and Processing: A Review0
An Effective Anti-Aliasing Approach for Residual Networks0
An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering0
An Empirical Study of Batch Normalization and Group Normalization in Conditional Computation0
An Enhanced Privacy-preserving Federated Few-shot Learning Framework for Respiratory Disease Diagnosis0
Show:102550
← PrevPage 36 of 60Next →

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