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 27512800 of 2964 papers

TitleStatusHype
An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the WildCode0
Text-driven Online Action DetectionCode0
Fixed-MAML for Few Shot Classification in Multilingual Speech Emotion RecognitionCode0
Fine-grained Contract NER using instruction based modelCode0
Finding Task-Relevant Features for Few-Shot Learning by Category TraversalCode0
FIGR: Few-shot Image Generation with ReptileCode0
Self-Augmented In-Context Learning for Unsupervised Word TranslationCode0
CORE: A Retrieve-then-Edit Framework for Counterfactual Data GenerationCode0
Few 'Zero Level Set'-Shot Learning of Shape Signed Distance Functions in Feature SpaceCode0
FewTopNER: Integrating Few-Shot Learning with Topic Modeling and Named Entity Recognition in a Multilingual FrameworkCode0
FewSOL: A Dataset for Few-Shot Object Learning in Robotic EnvironmentsCode0
Text-Guided Mixup Towards Long-Tailed Image CategorizationCode0
Few-Shot Transfer Learning to improve Chest X-Ray pathology detection using limited tripletsCode0
Few-Shot Text Classification with Pre-Trained Word Embeddings and a Human in the LoopCode0
Text-to-feature diffusion for audio-visual few-shot learningCode0
AffinityNet: semi-supervised few-shot learning for disease type predictionCode0
Offline Handwritten Amharic Character Recognition Using Few-shot LearningCode0
OmniNet: Omnidirectional Representations from TransformersCode0
Cooperative Bi-path Metric for Few-shot LearningCode0
Texture or Semantics? Vision-Language Models Get Lost in Font RecognitionCode0
Few-Shot Point Cloud Region Annotation with Human in the LoopCode0
Continuous max-flow augmentation of self-supervised few-shot learning on SPECT left ventriclesCode0
TGDM: Target Guided Dynamic Mixup for Cross-Domain Few-Shot LearningCode0
TGG: Transferable Graph Generation for Zero-shot and Few-shot LearningCode0
On convex decision regions in deep network representationsCode0
WikiGoldSK: Annotated Dataset, Baselines and Few-Shot Learning Experiments for Slovak Named Entity RecognitionCode0
Self-Supervised Few-Shot Learning for Ischemic Stroke Lesion SegmentationCode0
Few-shot Novel Category DiscoveryCode0
Few-Shot NLG with Pre-Trained Language ModelCode0
Expanding continual few-shot learning benchmarks to include recognition of specific instancesCode0
Continual Few-Shot Learning for Text ClassificationCode0
Few-Shot Multilingual Open-Domain QA from 5 ExamplesCode0
Continual Adversarial DefenseCode0
Few-shot link prediction via graph neural networks for Covid-19 drug-repurposingCode0
Contextualizing Enhances Gradient Based Meta LearningCode0
One Representative-Shot Learning Using a Population-Driven Template with Application to Brain Connectivity Classification and Evolution PredictionCode0
Self-Supervised Learning For Few-Shot Image ClassificationCode0
Multi-Objective Linear Ensembles for Robust and Sparse Training of Few-Bit Neural NetworksCode0
Few-Shot Learning with Graph Neural NetworksCode0
Self-Supervised Learning from Contrastive Mixtures for Personalized Speech EnhancementCode0
Self-supervised Meta-Prompt Learning with Meta-Gradient Regularization for Few-shot GeneralizationCode0
Few-Shot Learning with Graph Neural NetworksCode0
Few-Shot Learning with Global Class RepresentationsCode0
Zero-shot Relation Classification from Side InformationCode0
Benchmarking Pathology Foundation Models: Adaptation Strategies and ScenariosCode0
Contextual Interaction via Primitive-based Adversarial Training For Compositional Zero-shot LearningCode0
Uncertainty-Aware Meta-Learning for Multimodal Task DistributionsCode0
Few-shot learning via tensor hallucinationCode0
Online Unsupervised Learning of Visual Representations and CategoriesCode0
On Measuring the Intrinsic Few-Shot Hardness of DatasetsCode0
Show:102550
← PrevPage 56 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