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 28012850 of 2964 papers

TitleStatusHype
MFNet: Multi-class Few-shot Segmentation Network with Pixel-wise Metric Learning0
MFORT-QA: Multi-hop Few-shot Open Rich Table Question Answering0
MGIMN: Multi-Grained Interactive Matching Network for Few-shot Text Classification0
MHFC: Multi-Head Feature Collaboration for Few-Shot Learning0
MICK: A Meta-Learning Framework for Few-shot Relation Classification with Small Training Data0
Mining Open Semantics from CLIP: A Relation Transition Perspective for Few-Shot Learning0
Mining the Explainability and Generalization: Fact Verification Based on Self-Instruction0
Misclassification Detection via Class Augmentation0
MixPro: Simple yet Effective Data Augmentation for Prompt-based Learning0
Mixture-of-Prompt-Experts for Multi-modal Semantic Understanding0
Mixture of Prompt Learning for Vision Language Models0
MODA: Motion-Drift Augmentation for Inertial Human Motion Analysis0
Model-Agnostic Graph Regularization for Few-Shot Learning0
Human-like Few-Shot Learning via Bayesian Reasoning over Natural Language0
Unleashing the Power of Shared Label Structures for Human Activity Recognition0
Modeling Political Orientation of Social Media Posts: An Extended Analysis0
Modelling Latent Skills for Multitask Language Generation0
Model Tuning or Prompt Tuning? A Study of Large Language Models for Clinical Concept and Relation Extraction0
Model X-Ray: Detection of Hidden Malware in AI Model Weights using Few Shot Learning0
mOSCAR: A Large-scale Multilingual and Multimodal Document-level Corpus0
MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learning0
Multi-Agent RL-Based Industrial AIGC Service Offloading over Wireless Edge Networks0
Multi-Class Few Shot Learning Task and Controllable Environment0
Multi-Distillation from Speech and Music Representation Models0
Multi-Domain Few-Shot Learning and Dataset for Agricultural Applications0
Multi-Label Few-Shot Learning for Aspect Category Detection0
Multi-Level Contrastive Learning for Few-Shot Problems0
Multi-Level Fine-Tuning, Data Augmentation, and Few-Shot Learning for Specialized Cyber Threat Intelligence0
Multilingual and Cross-Lingual Intent Detection from Spoken Data0
Multilingual Few-Shot Learning via Language Model Retrieval0
Multimodal Cross-Domain Few-Shot Learning for Egocentric Action Recognition0
Multimodal Few-Shot Learning with Frozen Language Models0
Multi-Modal Few-Shot Object Detection with Meta-Learning-Based Cross-Modal Prompting0
Multimodality in Meta-Learning: A Comprehensive Survey0
Multimodal Prototypical Networks for Few-shot Learning0
Multi-Objective Few-shot Learning for Fair Classification0
Multi-Objective Meta Learning0
Multi-Objective Optimization Using Adaptive Distributed Reinforcement Learning0
Multi-Representation Ensemble in Few-Shot Learning0
Multi-scale Adaptive Task Attention Network for Few-Shot Learning0
Multi-step Estimation for Gradient-based Meta-learning0
Multi-unit soft sensing permits few-shot learning0
Museum Exhibit Identification Challenge for Domain Adaptation and Beyond0
Museum Exhibit Identification Challenge for the Supervised Domain Adaptation and Beyond0
Music auto-tagging in the long tail: A few-shot approach0
Mutual CRF-GNN for Few-Shot Learning0
Mutual Reinforcement Effects in Japanese Sentence Classification and Named Entity Recognition Tasks0
Naive Few-Shot Learning: Uncovering the fluid intelligence of machines0
NAVCON: A Cognitively Inspired and Linguistically Grounded Corpus for Vision and Language Navigation0
NDPNet: A novel non-linear data projection network for few-shot fine-grained image classification0
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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