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 601650 of 2964 papers

TitleStatusHype
BOIL: Towards Representation Change for Few-shot LearningCode1
Domain Generalizer: A Few-shot Meta Learning Framework for Domain Generalization in Medical ImagingCode1
Learning to Reason in Round-based Games: Multi-task Sequence Generation for Purchasing Decision Making in First-person ShootersCode1
An Overview of Deep Learning Architectures in Few-Shot Learning DomainCode1
Few Shot Learning Framework to Reduce Inter-observer Variability in Medical ImagesCode1
Few-shot Classification via Adaptive AttentionCode1
Model-Agnostic Boundary-Adversarial Sampling for Test-Time Generalization in Few-Shot learningCode1
A Comprehensive Evaluation of Multi-task Learning and Multi-task Pre-training on EHR Time-series DataCode1
Fuzzy Graph Neural Network for Few-Shot LearningCode1
Explanation-Guided Training for Cross-Domain Few-Shot ClassificationCode1
How to trust unlabeled data? Instance Credibility Inference for Few-Shot LearningCode1
Concept Learners for Few-Shot LearningCode1
Wandering Within a World: Online Contextualized Few-Shot LearningCode1
Generalized Few-Shot Video Classification with Video Retrieval and Feature GenerationCode1
Evaluation for Weakly Supervised Object Localization: Protocol, Metrics, and DatasetsCode1
Online probabilistic label treesCode1
Few-Shot One-Class Classification via Meta-LearningCode1
Covariate Distribution Aware Meta-learningCode1
Laplacian Regularized Few-Shot LearningCode1
Few-Shot Microscopy Image Cell SegmentationCode1
Laplacian Regularized Few-Shot LearningCode1
Many-Class Few-Shot Learning on Multi-Granularity Class HierarchyCode1
Graph Prototypical Networks for Few-shot Learning on Attributed NetworksCode1
Self-Supervised Prototypical Transfer Learning for Few-Shot ClassificationCode1
Self-supervised Knowledge Distillation for Few-shot LearningCode1
Graph Meta Learning via Local SubgraphsCode1
Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection NetworkCode1
Multi-view Contrastive Learning for Online Knowledge DistillationCode1
Leveraging the Feature Distribution in Transfer-based Few-Shot LearningCode1
Interpretable Time-series Classification on Few-shot SamplesCode1
Adaptive Subspaces for Few-Shot LearningCode1
Attentive Weights Generation for Few Shot Learning via Information MaximizationCode1
Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational RepresentationsCode1
Boosting on the shoulders of giants in quantum device calibrationCode1
SOLOIST: Building Task Bots at Scale with Transfer Learning and Machine TeachingCode1
Harvesting and Refining Question-Answer Pairs for Unsupervised QACode1
DenoiSeg: Joint Denoising and SegmentationCode1
Towards Fast Adaptation of Neural Architectures with Meta LearningCode1
Few-Shot Learning for Opinion SummarizationCode1
Physarum Powered Differentiable Linear Programming Layers and ApplicationsCode1
Learning to Learn to Disambiguate: Meta-Learning for Few-Shot Word Sense DisambiguationCode1
Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection SegmentationCode1
Supervised Domain Adaptation: A Graph Embedding Perspective and a Rectified Experimental ProtocolCode1
Defining Benchmarks for Continual Few-Shot LearningCode1
Few-Shot Single-View 3-D Object Reconstruction with Compositional PriorsCode1
Diversity Helps: Unsupervised Few-shot Learning via Distribution Shift-based Data AugmentationCode1
Meta-Learning in Neural Networks: A SurveyCode1
From Generalized zero-shot learning to long-tail with class descriptorsCode1
Learning to Segment the TailCode1
DPGN: Distribution Propagation Graph Network for Few-shot LearningCode1
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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