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 25012550 of 2964 papers

TitleStatusHype
MEAL: Stable and Active Learning for Few-Shot PromptingCode0
Measuring Dataset GranularityCode0
Unsupervised Attention Mechanism across Neural Network LayersCode0
Regularization of Distinct Strategies for Unsupervised Question GenerationCode0
Measuring the Robustness of NLP Models to Domain ShiftsCode0
Regularized Contrastive Pre-training for Few-shot Bioacoustic Sound DetectionCode0
Interactive Symbol Grounding with Complex Referential ExpressionsCode0
Intelligence, physics and information -- the tradeoff between accuracy and simplicity in machine learningCode0
MEDFORM: A Foundation Model for Contrastive Learning of CT Imaging and Clinical Numeric Data in Multi-Cancer AnalysisCode0
Dynamic Sub-Cluster-Aware Network for Few-Shot Skin Disease ClassificationCode0
IntellectSeeker: A Personalized Literature Management System with the Probabilistic Model and Large Language ModelCode0
Subspace Adaptation Prior for Few-Shot LearningCode0
Instance Selection Mechanisms for Human-in-the-Loop Systems in Few-Shot LearningCode0
Reinforcement Learning for Few-Shot Text Generation AdaptationCode0
Regression Networks for Meta-Learning Few-Shot ClassificationCode0
DEff-GAN: Diverse Attribute Transfer for Few-Shot Image SynthesisCode0
A separability-based approach to quantifying generalization: which layer is best?Code0
Memory-Efficient Fine-Tuning of Transformers via Token SelectionCode0
Relation Extraction as Open-book Examination: Retrieval-enhanced Prompt TuningCode0
Relation Extraction in underexplored biomedical domains: A diversity-optimised sampling and synthetic data generation approachCode0
Certification of Speaker Recognition Models to Additive PerturbationsCode0
MenuCraft: Interactive Menu System Design with Large Language ModelsCode0
Remote Task-oriented Grasp Area Teaching By Non-Experts through Interactive Segmentation and Few-Shot LearningCode0
Are Few-Shot Learning Benchmarks too Simple ? Solving them without Task Supervision at Test-TimeCode0
Reordering Examples Helps during Priming-based Few-Shot LearningCode0
Instance-level Few-shot Learning with Class Hierarchy MiningCode0
CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot LearningCode0
Meta-Adapters: Parameter Efficient Few-shot Fine-tuning through Meta-LearningCode0
Instance-aware Prompt Learning for Language Understanding and GenerationCode0
Supervision and Source Domain Impact on Representation Learning: A Histopathology Case StudyCode0
Is LLM an Overconfident Judge? Unveiling the Capabilities of LLMs in Detecting Offensive Language with Annotation DisagreementCode0
Defensive Few-shot LearningCode0
SIP: Injecting a Structural Inductive Bias into a Seq2Seq Model by SimulationCode0
Towards Enabling Meta-Learning from Target ModelsCode0
Cancer Vaccine Adjuvant Name Recognition from Biomedical Literature using Large Language ModelsCode0
Meta-CurvatureCode0
Repurposing Pretrained Models for Robust Out-of-domain Few-Shot LearningCode0
Argument Mining in Data Scarce Settings: Cross-lingual Transfer and Few-shot TechniquesCode0
Inferring Latent Class Statistics from Text for Robust Visual Few-Shot LearningCode0
Deep Content Understanding Toward Entity and Aspect Target Sentiment Analysis on Foundation ModelsCode0
RelationNet2: Deep Comparison Columns for Few-Shot LearningCode0
A Baseline for Few-Shot Image ClassificationCode0
SVasP: Self-Versatility Adversarial Style Perturbation for Cross-Domain Few-Shot LearningCode0
Decoder Choice Network for Meta-LearningCode0
Incremental Few-Shot Learning with Attention Attractor NetworksCode0
In-context Learning and Gradient Descent RevisitedCode0
Meta Fine-Tuning Neural Language Models for Multi-Domain Text MiningCode0
Dealing With Heterogeneous 3D MR Knee Images: A Federated Few-Shot Learning Method With Dual Knowledge DistillationCode0
MetaGAD: Meta Representation Adaptation for Few-Shot Graph Anomaly DetectionCode0
Swiss DINO: Efficient and Versatile Vision Framework for On-device Personal Object SearchCode0
Show:102550
← PrevPage 51 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