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 10011050 of 2964 papers

TitleStatusHype
Large-Scale Few-Shot Learning: Knowledge Transfer With Class HierarchyCode0
Detecting Statements in Text: A Domain-Agnostic Few-Shot SolutionCode0
Compositional Clustering: Applications to Multi-Label Object Recognition and Speaker IdentificationCode0
Regularization of Distinct Strategies for Unsupervised Question GenerationCode0
Beyond Interpretability: The Gains of Feature Monosemanticity on Model RobustnessCode0
Reinforcement Learning for Few-Shot Text Generation AdaptationCode0
Large Language Models as Attribution Regularizers for Efficient Model TrainingCode0
Large Language Models Vote: Prompting for Rare Disease IdentificationCode0
Compositional Generalization for Primitive SubstitutionsCode0
LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot LearningCode0
Large Language Models are biased to overestimate profoundnessCode0
Descriptor and Word Soups: Overcoming the Parameter Efficiency Accuracy Tradeoff for Out-of-Distribution Few-shot LearningCode0
Knowledge Graph Transfer Network for Few-Shot RecognitionCode0
Knowledge-Enhanced Multi-Label Few-Shot Product Attribute-Value ExtractionCode0
Demonstration-based learning for few-shot biomedical named entity recognition under machine reading comprehensionCode0
Memorisation versus Generalisation in Pre-trained Language ModelsCode0
Kernel Relative-prototype Spectral Filtering for Few-shot LearningCode0
Delta-encoder: an effective sample synthesis method for few-shot object recognitionCode0
Learning Adaptive Classifiers Synthesis for Generalized Few-Shot LearningCode0
Benchmarking Spurious Bias in Few-Shot Image ClassifiersCode0
Benchmarking Pathology Foundation Models: Adaptation Strategies and ScenariosCode0
Revisiting In-context Learning Inference Circuit in Large Language ModelsCode0
Investigating the Effect of Natural Language Explanations on Out-of-Distribution Generalization in Few-shot NLICode0
DEff-GAN: Diverse Attribute Transfer for Few-Shot Image SynthesisCode0
Defensive Few-shot LearningCode0
Interval Bound Interpolation for Few-shot Learning with Few TasksCode0
Interactive Symbol Grounding with Complex Referential ExpressionsCode0
IntellectSeeker: A Personalized Literature Management System with the Probabilistic Model and Large Language ModelCode0
Meta Architecture SearchCode0
Intelligence, physics and information -- the tradeoff between accuracy and simplicity in machine learningCode0
Joint Graph Learning and Model Fitting in Laplacian Regularized Stratified ModelsCode0
Instance-aware Prompt Learning for Language Understanding and GenerationCode0
A Maximum Log-Likelihood Method for Imbalanced Few-Shot Learning TasksCode0
SIP: Injecting a Structural Inductive Bias into a Seq2Seq Model by SimulationCode0
Instance-level Few-shot Learning with Class Hierarchy MiningCode0
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
Instance Selection Mechanisms for Human-in-the-Loop Systems in Few-Shot LearningCode0
A Zero-Shot LLM Framework for Automatic Assignment Grading in Higher EducationCode0
Decoder Choice Network for Meta-LearningCode0
Dealing With Heterogeneous 3D MR Knee Images: A Federated Few-Shot Learning Method With Dual Knowledge DistillationCode0
In-context Learning and Gradient Descent RevisitedCode0
Incremental Few-Shot Learning with Attention Attractor NetworksCode0
Kajal: Extracting Grammar of a Source Code Using Large Language ModelsCode0
Learning Deep Disentangled Embeddings with the F-Statistic LossCode0
Dataset2Vec: Learning Dataset Meta-FeaturesCode0
When Low Resource NLP Meets Unsupervised Language Model: Meta-pretraining Then Meta-learning for Few-shot Text ClassificationCode0
Data-Efficient Language Shaped Few-shot Image ClassificationCode0
Adaptive Prototypical NetworksCode0
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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