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 22512300 of 2964 papers

TitleStatusHype
Fixed-MAML for Few Shot Classification in Multilingual Speech Emotion RecognitionCode0
Z-Score Normalization, Hubness, and Few-Shot Learning0
Coarsely-Labeled Data for Better Few-Shot TransferCode0
Curvature Generation in Curved Spaces for Few-Shot Learning0
Kernel Methods in Hyperbolic Spaces0
Learning To Hallucinate Examples From Extrinsic and Intrinsic Supervision0
Meta-Attack: Class-Agnostic and Model-Agnostic Physical Adversarial Attack0
Pseudo-Loss Confidence Metric for Semi-Supervised Few-Shot Learning0
Task-Aware Part Mining Network for Few-Shot Learning0
Robust Meta-learning with Noise via Eigen-Reptile0
Meta-Learned Confidence for Transductive Few-shot Learning0
MELR: Meta-Learning via Modeling Episode-Level Relationships for Few-Shot Learning0
Towards Understanding the Cause of Error in Few-Shot Learning0
Few-Round Learning for Federated Learning0
MASP: Model-Agnostic Sample Propagation for Few-shot learning0
Auto-view contrastive learning for few-shot image recognition0
Learning to Learn with Smooth Regularization0
A Lazy Approach to Long-Horizon Gradient-Based Meta-Learning0
cross-modal knowledge enhancement mechanism for few-shot learning0
Misclassification Detection via Class Augmentation0
MetaNorm: Learning to Normalize Few-Shot Batches Across Domains0
Incremental few-shot learning via vector quantization in deep embedded space0
IEPT: Instance-Level and Episode-Level Pretext Tasks for Few-Shot LearningCode1
Learning Semantic Similarities for Prototypical Classifiers0
Fewmatch: Dynamic Prototype Refinement for Semi-Supervised Few-Shot Learning0
Improve Novel Class Generalization By Adaptive Feature Distribution for Few-Shot Learning0
Class Imbalance in Few-Shot Learning0
Multi-Representation Ensemble in Few-Shot Learning0
Context-Agnostic Learning Using Synthetic Data0
Cut out the annotator, keep the cutout: better segmentation with weak supervision0
Attacking Few-Shot Classifiers with Adversarial Support Sets0
Constellation Nets for Few-Shot LearningCode1
Exploring representation learning for flexible few-shot tasks0
A Theory of Self-Supervised Framework for Few-Shot Learning0
On the Role of Pre-training for Meta Few-Shot Learning0
Making Pre-trained Language Models Better Few-shot LearnersCode1
A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters0
C-Norm: a neural approach to few-shot entity normalizationCode0
Few-Shot Named Entity Recognition: A Comprehensive StudyCode1
Compositional Prototype Network with Multi-view Comparision for Few-Shot Point Cloud Semantic Segmentation0
Power Normalizations in Fine-grained Image, Few-shot Image and Graph Classification0
Few Shot Learning With No Labels0
Spatial Contrastive Learning for Few-Shot ClassificationCode1
Task-Adaptive Negative Envision for Few-Shot Open-Set RecognitionCode1
Personalized Adaptive Meta Learning for Cold-start User Preference Prediction0
PTN: A Poisson Transfer Network for Semi-supervised Few-shot Learning0
Few-shot Sequence Learning with Transformers0
On Episodes, Prototypical Networks, and Few-shot LearningCode1
Iterative label cleaning for transductive and semi-supervised few-shot learningCode1
Extended Few-Shot Learning: Exploiting Existing Resources for Novel TasksCode1
Show:102550
← PrevPage 46 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