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 28512900 of 2964 papers

TitleStatusHype
Comprehensive Study: How the Context Information of Different Granularity Affects Dialogue State Tracking?Code0
Few-shot Learning as Cluster-induced Voronoi Diagrams: A Geometric ApproachCode0
Comprehensive Evaluation and Insights into the Use of Large Language Models in the Automation of Behavior-Driven Development Acceptance Test FormulationCode0
Few-Shot Image Recognition by Predicting Parameters from ActivationsCode0
ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-TranslationCode0
Parallel Corpus for Indigenous Language Translation: Spanish-Mazatec and Spanish-MixtecCode0
Parameter-Efficient Cross-lingual Transfer of Vision and Language Models via Translation-based AlignmentCode0
Few-shot human motion prediction for heterogeneous sensorsCode0
Few-Shot Electronic Health Record Coding through Graph Contrastive LearningCode0
Sequential Skip Prediction with Few-shot in Streamed Music ContentsCode0
Think Big, Teach Small: Do Language Models Distil Occam’s Razor?Code0
PARN: Position-Aware Relation Networks for Few-Shot LearningCode0
SgVA-CLIP: Semantic-guided Visual Adapting of Vision-Language Models for Few-shot Image ClassificationCode0
Few-shot Diagnosis of Chest x-rays Using an Ensemble of Random Discriminative SubspacesCode0
Compositional Generalization for Primitive SubstitutionsCode0
Compositional Clustering: Applications to Multi-Label Object Recognition and Speaker IdentificationCode0
Shoestring: Graph-Based Semi-Supervised Learning with Severely Limited Labeled DataCode0
Composing Neural Learning and Symbolic Reasoning with an Application to Visual DiscriminationCode0
Patent Figure Classification using Large Vision-language ModelsCode0
Complex Facial Expression Recognition Using Deep Knowledge Distillation of Basic FeaturesCode0
PCBERT: Parent and Child BERT for Chinese Few-shot NERCode0
Few shot clustering for indoor occupancy detection with extremely low-quality images from battery free camerasCode0
Few-Shot Classification of Skin Lesions from Dermoscopic Images by Meta-Learning Representative EmbeddingsCode0
AttenWalker: Unsupervised Long-Document Question Answering via Attention-based Graph WalkingCode0
SHOT: Suppressing the Hessian along the Optimization Trajectory for Gradient-Based Meta-LearningCode0
Complementing Representation Deficiency in Few-shot Image Classification: A Meta-Learning ApproachCode0
The Art of Camouflage: Few-Shot Learning for Animal Detection and SegmentationCode0
A Named Entity Recognition Corpus for Vietnamese Biomedical Texts to Support Tuberculosis TreatmentCode0
Few-shot Action Recognition with Permutation-invariant AttentionCode0
TNT: Text-Conditioned Network with Transductive Inference for Few-Shot Video ClassificationCode0
An AI-Powered VVPAT Counter for Elections in IndiaCode0
Perturbing the Gradient for Alleviating Meta OverfittingCode0
Attentional Meta-learners for Few-shot Polythetic ClassificationCode0
Uniform Sampling over Episode DifficultyCode0
Few-Shot Action Localization without Knowing BoundariesCode0
FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art EvaluationCode0
A Maximum Log-Likelihood Method for Imbalanced Few-Shot Learning TasksCode0
UniGen: Universal Domain Generalization for Sentiment Classification via Zero-shot Dataset GenerationCode0
Piecewise classifier mappings: Learning fine-grained learners for novel categories with few examplesCode0
Comparison-Based Convolutional Neural Networks for Cervical Cell/Clumps Detection in the Limited Data ScenarioCode0
Similarity of Classification TasksCode0
UniMatch: Universal Matching from Atom to Task for Few-Shot Drug DiscoveryCode0
Instant Response Few-shot Object Detection with Meta Strategy and Explicit Localization InferenceCode0
Compact Bilinear PoolingCode0
A Plug-in Method for Representation Factorization in Connectionist ModelsCode0
Coloring With Limited Data: Few-Shot Colorization via Memory-Augmented NetworksCode0
SIMPL: Generating Synthetic Overhead Imagery to Address Zero-shot and Few-Shot Detection ProblemsCode0
Few-Max: Few-Shot Domain Adaptation for Unsupervised Contrastive Representation LearningCode0
Few and Fewer: Learning Better from Few Examples Using Fewer Base ClassesCode0
FEDI: Few-shot learning based on Earth Mover's Distance algorithm combined with deep residual network to identify diabetic retinopathyCode0
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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