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
Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language InferenceCode1
Discriminative Sample-Guided and Parameter-Efficient Feature Space Adaptation for Cross-Domain Few-Shot LearningCode1
Deeply Coupled Cross-Modal Prompt LearningCode1
LASP: Text-to-Text Optimization for Language-Aware Soft Prompting of Vision & Language ModelsCode1
Interpretable Time-series Classification on Few-shot SamplesCode1
Contrastive Meta-Learning for Partially Observable Few-Shot LearningCode1
Interventional Few-Shot LearningCode1
Contrastive Prototypical Network with Wasserstein Confidence PenaltyCode1
DC-SAM: In-Context Segment Anything in Images and Videos via Dual ConsistencyCode1
EASE: Unsupervised Discriminant Subspace Learning for Transductive Few-Shot LearningCode1
Convolutional Bypasses Are Better Vision Transformer AdaptersCode1
ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft PromptsCode1
Domain-Adaptive Few-Shot LearningCode1
Domain Adaptive Few-Shot Open-Set LearningCode1
Domain Generalizer: A Few-shot Meta Learning Framework for Domain Generalization in Medical ImagingCode1
Learning Implicit Temporal Alignment for Few-shot Video ClassificationCode1
DPGN: Distribution Propagation Graph Network for Few-shot LearningCode1
Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled DataCode1
Learning to Compare: Relation Network for Few-Shot LearningCode1
Dual Adaptive Representation Alignment for Cross-domain Few-shot LearningCode1
Cost-effective Instruction Learning for Pathology Vision and Language AnalysisCode1
Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identificationCode1
Covariate Distribution Aware Meta-learningCode1
COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approachCode1
Integrative Few-Shot Learning for Classification and SegmentationCode1
Intriguing Properties of Vision TransformersCode1
Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-shot LearningCode1
Example-Based Named Entity RecognitionCode1
3D-IDS: Doubly Disentangled Dynamic Intrusion DetectionCode1
Data Distributional Properties Drive Emergent In-Context Learning in TransformersCode1
Instance Credibility Inference for Few-Shot LearningCode1
Efficient Few-shot Learning for Multi-label Classification of Scientific Documents with Many ClassesCode1
InstructionNER: A Multi-Task Instruction-Based Generative Framework for Few-shot NERCode1
Inductive Relation Prediction by BERTCode1
Elaborative Rehearsal for Zero-shot Action RecognitionCode1
Cross-Domain Few-Shot Learning by Representation FusionCode1
Enhancing Information Maximization with Distance-Aware Contrastive Learning for Source-Free Cross-Domain Few-Shot LearningCode1
Leveraging Large Language Models for Node Generation in Few-Shot Learning on Text-Attributed GraphsCode1
Enhancing Few-shot Image Classification with Cosine TransformerCode1
Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human LanguageCode1
CoNeRF: Controllable Neural Radiance FieldsCode1
Information Maximization for Few-Shot LearningCode1
Instruction Tuning for Few-Shot Aspect-Based Sentiment AnalysisCode1
LLM-Enhanced Bayesian Optimization for Efficient Analog Layout Constraint GenerationCode1
Inverse is Better! Fast and Accurate Prompt for Few-shot Slot TaggingCode1
Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language ModelsCode1
CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLPCode1
ESPT: A Self-Supervised Episodic Spatial Pretext Task for Improving Few-Shot LearningCode1
Evaluation for Weakly Supervised Object Localization: Protocol, Metrics, and DatasetsCode1
Cross-Modal Augmentation for Few-Shot Multimodal Fake News DetectionCode1
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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