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 26012650 of 2964 papers

TitleStatusHype
Meta-Meta Classification for One-Shot LearningCode0
Revisiting Few-Shot Learning from a Causal PerspectiveCode0
High-order structure preserving graph neural network for few-shot learningCode0
TADAM: Task dependent adaptive metric for improved few-shot learningCode0
Hierarchy-based Image Embeddings for Semantic Image RetrievalCode0
ACE: Anatomically Consistent Embeddings in Composition and DecompositionCode0
Hierarchical Variational Memory for Few-shot Learning Across DomainsCode0
Revisiting In-context Learning Inference Circuit in Large Language ModelsCode0
Metaphor Detection for Low Resource Languages: From Zero-Shot to Few-Shot Learning in Middle High GermanCode0
Deep Mixture of Experts via Shallow EmbeddingCode0
TAFE-Net: Task-Aware Feature Embeddings for Low Shot LearningCode0
Revisiting Local Descriptor based Image-to-Class Measure for Few-shot LearningCode0
PACIA: Parameter-Efficient Adapter for Few-Shot Molecular Property PredictionCode0
Hidden Entity Detection from GitHub Leveraging Large Language ModelsCode0
Boosting the Generalization Capability in Cross-Domain Few-shot Learning via Noise-enhanced Supervised AutoencoderCode0
Hebbian and Gradient-based Plasticity Enables Robust Memory and Rapid Learning in RNNsCode0
An Open-set Recognition and Few-Shot Learning Dataset for Audio Event Classification in Domestic EnvironmentsCode0
Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networksCode0
TAPE: Assessing Few-shot Russian Language UnderstandingCode0
Cross-lingual Approaches for the Detection of Adverse Drug Reactions in German from a Patient’s PerspectiveCode0
Implicit Geometry and Interaction Embeddings Improve Few-Shot Molecular Property PredictionCode0
Harnessing Large Language Models Over Transformer Models for Detecting Bengali Depressive Social Media Text: A Comprehensive StudyCode0
Cross-lingual Approaches for the Detection of Adverse Drug Reactions in German from a Patient's PerspectiveCode0
TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot LearningCode0
GSHOT: Few-shot Generative Modeling of Labeled GraphsCode0
How to Fine-tune Models with Few Samples: Update, Data Augmentation, and Test-time AugmentationCode0
Revisiting Unsupervised Meta-Learning via the Characteristics of Few-Shot TasksCode0
Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency ParsingCode0
Anomaly Multi-classification in Industrial Scenarios: Transferring Few-shot Learning to a New TaskCode0
RGL: A Simple yet Effective Relation Graph Augmented Prompt-based Tuning Approach for Few-Shot LearningCode0
Task-Adapter++: Task-specific Adaptation with Order-aware Alignment for Few-shot Action RecognitionCode0
Advancing Image Retrieval with Few-Shot Learning and Relevance FeedbackCode0
Grid Cell Path Integration For Movement-Based Visual Object RecognitionCode0
RIDE: Enhancing Large Language Model Alignment through Restyled In-Context Learning Demonstration ExemplarsCode0
Accelerating Convergence in Bayesian Few-Shot ClassificationCode0
MICM: Rethinking Unsupervised Pretraining for Enhanced Few-shot LearningCode0
Grasping the Essentials: Tailoring Large Language Models for Zero-Shot Relation ExtractionCode0
Mind the Gap: Evaluating Vision Systems in Small Data ApplicationsCode0
RLBench: The Robot Learning Benchmark & Learning EnvironmentCode0
A Comparative Study on Textual Saliency of Styles from Eye Tracking, Annotations, and Language ModelsCode0
Boosting keyword spotting through on-device learnable user speech characteristicsCode0
MISE: Meta-knowledge Inheritance for Social Media-Based Stressor EstimationCode0
MI-VisionShot: Few-shot adaptation of vision-language models for slide-level classification of histopathological imagesCode0
Graph Few-shot Learning with Task-specific StructuresCode0
Boosting Few-Shot Visual Learning with Self-SupervisionCode0
Task Affinity with Maximum Bipartite Matching in Few-Shot LearningCode0
A Flag Decomposition for Hierarchical DatasetsCode0
Mixture of Soft Prompts for Controllable Data GenerationCode0
Robust Few-Shot Ensemble Learning with Focal Diversity-Based PruningCode0
Graph Few-shot Learning via Knowledge TransferCode0
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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