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 551600 of 2964 papers

TitleStatusHype
A New Look and Convergence Rate of Federated Multi-Task Learning with Laplacian RegularizationCode1
COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approachCode1
Transfer learning based few-shot classification using optimal transport mapping from preprocessed latent space of backbone neural networkCode1
Generalising via Meta-Examples for Continual Learning in the WildCode1
Few-Shot Semantic Parsing for New PredicatesCode1
Improving Few-Shot Learning with Auxiliary Self-Supervised Pretext TasksCode1
Free Lunch for Few-shot Learning: Distribution CalibrationCode1
Shallow Bayesian Meta Learning for Real-World Few-Shot RecognitionCode1
Few-Shot Learning with Class ImbalanceCode1
IEPT: Instance-Level and Episode-Level Pretext Tasks for Few-Shot LearningCode1
Constellation Nets for Few-Shot LearningCode1
Making Pre-trained Language Models Better Few-shot LearnersCode1
Few-Shot Named Entity Recognition: A Comprehensive StudyCode1
Spatial Contrastive Learning for Few-Shot ClassificationCode1
Task-Adaptive Negative Envision for Few-Shot Open-Set RecognitionCode1
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
Fine-grained Angular Contrastive Learning with Coarse LabelsCode1
Few-Shot Classification with Feature Map Reconstruction NetworksCode1
Information Maximization for Few-Shot LearningCode1
TableGPT: Few-shot Table-to-Text Generation with Table Structure Reconstruction and Content MatchingCode1
BSNet: Bi-Similarity Network for Few-shot Fine-grained Image ClassificationCode1
IFSS-Net: Interactive Few-Shot Siamese Network for Faster Muscle Segmentation and Propagation in Volumetric UltrasoundCode1
How Well Do Self-Supervised Models Transfer?Code1
Match Them Up: Visually Explainable Few-shot Image ClassificationCode1
Investigating Novel Verb Learning in BERT: Selectional Preference Classes and Alternation-Based Syntactic GeneralizationCode1
Supervised Contrastive Learning for Pre-trained Language Model Fine-tuningCode1
CMT in TREC-COVID Round 2: Mitigating the Generalization Gaps from Web to Special Domain SearchCode1
Meta-Learning with Adaptive HyperparametersCode1
Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language InferenceCode1
Few-shot Decoding of Brain Activation MapsCode1
Restoring Negative Information in Few-Shot Object DetectionCode1
Self-training for Few-shot Transfer Across Extreme Task DifferencesCode1
The Tatoeba Translation Challenge -- Realistic Data Sets for Low Resource and Multilingual MTCode1
Cross-Domain Few-Shot Learning by Representation FusionCode1
Self-training Improves Pre-training for Natural Language UnderstandingCode1
Self-Supervised Few-Shot Learning on Point CloudsCode1
Interventional Few-Shot LearningCode1
A Few-shot Learning Approach for Historical Ciphered Manuscript RecognitionCode1
Vector Projection Network for Few-shot Slot Tagging in Natural Language UnderstandingCode1
Few-Shot Unsupervised Continual Learning through Meta-ExamplesCode1
'Less Than One'-Shot Learning: Learning N Classes From M<N SamplesCode1
Synbols: Probing Learning Algorithms with Synthetic DatasetsCode1
Few-shot Learning with LSSVM Base Learner and Transductive ModulesCode1
Prototype Completion with Primitive Knowledge for Few-Shot LearningCode1
Region Comparison Network for Interpretable Few-shot Image ClassificationCode1
GPU-based Self-Organizing Maps for Post-Labeled Few-Shot Unsupervised LearningCode1
Transductive Information Maximization For Few-Shot LearningCode1
Example-Based Named Entity RecognitionCode1
Show:102550
← PrevPage 12 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