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 23512400 of 2964 papers

TitleStatusHype
MCML: A Novel Memory-based Contrastive Meta-Learning Method for Few Shot Slot Tagging0
MDFL: A UNIFIED FRAMEWORK WITH META-DROPOUT FOR FEW-SHOT LEARNING0
MDFM: Multi-Decision Fusing Model for Few-Shot Learning0
Measuring Sustainability Intention of ESG Fund Disclosure using Few-Shot Learning0
Measuring the Effect of Causal Disentanglement on the Adversarial Robustness of Neural Network Models0
Mechanistic Fine-tuning for In-context Learning0
Medical Coding with Biomedical Transformer Ensembles and Zero/Few-shot Learning0
MedSAGa: Few-shot Memory Efficient Medical Image Segmentation using Gradient Low-Rank Projection in SAM0
MELR: Meta-Learning via Modeling Episode-Level Relationships for Few-Shot Learning0
Membership Inference Attacks fueled by Few-Short Learning to detect privacy leakage tackling data integrity0
Memory-Augmented Relation Network for Few-Shot Learning0
Memory-Based Neighbourhood Embedding for Visual Recognition0
Memory Is All You Need: Testing How Model Memory Affects LLM Performance in Annotation Tasks0
MemREIN: Rein the Domain Shift for Cross-Domain Few-Shot Learning0
MENTOR: Multilingual tExt detectioN TOward leaRning by analogy0
MerA: Merging Pretrained Adapters For Few-Shot Learning0
Message Passing Neural Processes0
Meta Adaptation using Importance Weighted Demonstrations0
Meta-Adapter: Parameter Efficient Few-Shot Learning through Meta-Learning0
Learning Meta Model for Zero- and Few-shot Face Anti-spoofing0
Méta-apprentissage : classification de messages en catégories émotionnelles inconnues en entraînement (Meta-learning : Classifying Messages into Unseen Emotional Categories)0
Meta-Attack: Class-Agnostic and Model-Agnostic Physical Adversarial Attack0
Metacognition-Enhanced Few-Shot Prompting With Positive Reinforcement0
MetaConcept: Learn to Abstract via Concept Graph for Weakly-Supervised Few-Shot Learning0
Meta Cyclical Annealing Schedule: A Simple Approach to Avoiding Meta-Amortization Error0
Learning to Propagate for Graph Meta-LearningCode0
Learning to Propagate Labels: Transductive Propagation Network for Few-shot LearningCode0
Learning to Learn Variational Semantic MemoryCode0
Bayesian Active Meta-Learning for Few Pilot Demodulation and EqualizationCode0
Learning to Learn Kernels with Variational Random FeaturesCode0
Enhancing Unsupervised Graph Few-shot Learning via Set Functions and Optimal TransportCode0
Enhancing Masked Time-Series Modeling via Dropping PatchesCode0
Learning to Learn By Self-CritiqueCode0
End-to-end Generative Zero-shot Learning via Few-shot LearningCode0
Learning to learn via Self-CritiqueCode0
Efficient Transfer Learning for Video-language Foundation ModelsCode0
A Statistical Model for Predicting Generalization in Few-Shot ClassificationCode0
Probing Predictions on OOD Images via Nearest CategoriesCode0
Effectiveness of Cross-linguistic Extraction of Genetic Information using Generative Large Language ModelsCode0
AI-Assisted Colonoscopy: Polyp Detection and Segmentation using Foundation ModelsCode0
Two Sides of Meta-Learning Evaluation: In vs. Out of DistributionCode0
Edge-labeling Graph Neural Network for Few-shot LearningCode0
Edge-Labeling based Directed Gated Graph Network for Few-shot LearningCode0
Learning to Forget for Meta-LearningCode0
ClimateX: Do LLMs Accurately Assess Human Expert Confidence in Climate Statements?Code0
Leveraging Bottom-Up and Top-Down Attention for Few-Shot Object DetectionCode0
Zero-shot Approach to Overcome Perturbation Sensitivity of PromptsCode0
Learning to Find Common Objects Across Few Image CollectionsCode0
Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category GraphCode0
Leveraging Large Language Models for Scalable Vector Graphics-Driven Image UnderstandingCode0
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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