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 2701–2750 of 2964 papers

TitleStatusHype
Loss meta-learning for forecasting—0
Low-Data Classification of Historical Music Manuscripts: A Few-Shot Learning Approach—0
Low-Rank Pairwise Alignment Bilinear Network For Few-Shot Fine-Grained Image Classification—0
Low-Resource Cross-Lingual Summarization through Few-Shot Learning with Large Language Models—0
Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey—0
Low Resource Pipeline for Spoken Language Understanding via Weak Supervision—0
Low-Shot Learning from Imaginary 3D Model—0
Incremental Few-Shot Object Detection for Robotics—0
LSFSL: Leveraging Shape Information in Few-shot Learning—0
LUWA Dataset: Learning Lithic Use-Wear Analysis on Microscopic Images—0
Machine learning with limited data—0
Magnetic Resonance Image Processing Transformer for General Accelerated Image Reconstruction—0
MahiNet: A Neural Network for Many-Class Few-Shot Learning with Class Hierarchy—0
Making Large Vision Language Models to be Good Few-shot Learners—0
Making Pre-trained Language Models End-to-end Few-shot Learners with Contrastive Prompt Tuning—0
Making Small Language Models Better Few-Shot Learners—0
Malware Classification from Memory Dumps Using Machine Learning, Transformers, and Large Language Models—0
MAML and ANIL Provably Learn Representations—0
Manual Verbalizer Enrichment for Few-Shot Text Classification—0
Many-Shot In-Context Learning—0
MapRE: An Effective Semantic Mapping Approach for Low-resource Relation Extraction—0
Mashee at SemEval-2024 Task 8: The Impact of Samples Quality on the Performance of In-Context Learning for Machine Text Classification—0
Masked Generative Extractor for Synergistic Representation and 3D Generation of Point Clouds—0
Mask-guided BERT for Few Shot Text Classification—0
Mask-guided Vision Transformer (MG-ViT) for Few-Shot Learning—0
MASP: Model-Agnostic Sample Propagation for Few-shot learning—0
MaTableGPT: GPT-based Table Data Extractor from Materials Science Literature—0
Few-shot Partial Multi-view Learning—0
MCML: A Novel Memory-based Contrastive Meta-Learning Method for Few Shot Slot Tagging—0
MDFL: A UNIFIED FRAMEWORK WITH META-DROPOUT FOR FEW-SHOT LEARNING—0
MDFM: Multi-Decision Fusing Model for Few-Shot Learning—0
Measuring Sustainability Intention of ESG Fund Disclosure using Few-Shot Learning—0
Measuring the Effect of Causal Disentanglement on the Adversarial Robustness of Neural Network Models—0
Mechanistic Fine-tuning for In-context Learning—0
Medical Coding with Biomedical Transformer Ensembles and Zero/Few-shot Learning—0
MedSAGa: Few-shot Memory Efficient Medical Image Segmentation using Gradient Low-Rank Projection in SAM—0
MELR: Meta-Learning via Modeling Episode-Level Relationships for Few-Shot Learning—0
Membership Inference Attacks fueled by Few-Short Learning to detect privacy leakage tackling data integrity—0
Memory-Augmented Relation Network for Few-Shot Learning—0
Memory-Based Neighbourhood Embedding for Visual Recognition—0
Memory Is All You Need: Testing How Model Memory Affects LLM Performance in Annotation Tasks—0
MemREIN: Rein the Domain Shift for Cross-Domain Few-Shot Learning—0
MENTOR: Multilingual tExt detectioN TOward leaRning by analogy—0
MerA: Merging Pretrained Adapters For Few-Shot Learning—0
Message Passing Neural Processes—0
Meta Adaptation using Importance Weighted Demonstrations—0
Meta-Adapter: Parameter Efficient Few-Shot Learning through Meta-Learning—0
Learning Meta Model for Zero- and Few-shot Face Anti-spoofing—0
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 Attack—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