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

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Showing 2051–2100 of 2964 papers

TitleStatusHype
Dataset Bias Prediction for Few-Shot Image Classification—0
DCP: Learning Accelerator Dataflow for Neural Network via Propagation—0
Decomposed Prototype Learning for Few-Shot Scene Graph Generation—0
DeepCode AI Fix: Fixing Security Vulnerabilities with Large Language Models—0
DeepCRCEval: Revisiting the Evaluation of Code Review Comment Generation—0
Deep Double-Side Learning Ensemble Model for Few-Shot Parkinson Speech Recognition—0
Deep few-shot learning for bi-temporal building change detection—0
TEGEE: Task dEfinition Guided Expert Ensembling for Generalizable and Few-shot Learning—0
Deep learning based detection of collateral circulation in coronary angiographies—0
Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey—0
Deep Learning of Unified Region, Edge, and Contour Models for Automated Image Segmentation—0
Deep Meta-Learning: Learning to Learn in the Concept Space—0
Deep metric learning improves lab of origin prediction of genetically engineered plasmids—0
Deep Transfer Learning with Graph Neural Network for Sensor-Based Human Activity Recognition—0
Deficiency of Large Language Models in Finance: An Empirical Examination of Hallucination—0
Dehazing Remote Sensing and UAV Imagery: A Review of Deep Learning, Prior-based, and Hybrid Approaches—0
Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Prompts—0
DemoGrasp: Few-Shot Learning for Robotic Grasping with Human Demonstration—0
DemoShapley: Valuation of Demonstrations for In-Context Learning—0
Demystification of Few-shot and One-shot Learning—0
Demystifying Prompts in Language Models via Perplexity Estimation—0
Dense Classification and Implanting for Few-Shot Learning—0
Dental Severity Assessment through Few-shot Learning and SBERT Fine-tuning—0
Dependable Neural Networks for Safety Critical Tasks—0
Derm-T2IM: Harnessing Synthetic Skin Lesion Data via Stable Diffusion Models for Enhanced Skin Disease Classification using ViT and CNN—0
Designing Informative Metrics for Few-Shot Example Selection—0
Detecting Actionable Requests and Offers on Social Media During Crises Using LLMs—0
Detecting Endangered Marine Species in Autonomous Underwater Vehicle Imagery Using Point Annotations and Few-Shot Learning—0
DGP-Net: Dense Graph Prototype Network for Few-Shot SAR Target Recognition—0
Cap2Aug: Caption guided Image to Image data Augmentation—0
Differentiable Entailment for Parameter Efficient Few Shot Learning—0
Differentiable Meta-learning Model for Few-shot Semantic Segmentation—0
Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs—0
Differentially Private Meta-Learning—0
A Survey of Diffusion Models in Natural Language Processing—0
Directed Variational Cross-encoder Network for Few-shot Multi-image Co-segmentation—0
DiSa: Directional Saliency-Aware Prompt Learning for Generalizable Vision-Language Models—0
Discrete Few-Shot Learning for Pan Privacy—0
Discriminative Few-Shot Learning Based on Directional Statistics—0
Disentangled Generation with Information Bottleneck for Few-Shot Learning—0
Disentangling Questions from Query Generation for Task-Adaptive Retrieval—0
Distillation of encoder-decoder transformers for sequence labelling—0
Distilling Large Language Models for Network Active Queue Management—0
Distributed Rule Vectors is A Key Mechanism in Large Language Models' In-Context Learning—0
Distributionally Robust Weighted k-Nearest Neighbors—0
Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Study—0
Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLP—0
Diversity Covariance-Aware Prompt Learning for Vision-Language Models—0
Diversity Over Quantity: A Lesson From Few Shot Relation Classification—0
Dizygotic Conditional Variational AutoEncoder for Multi-Modal and Partial Modality Absent Few-Shot Learning—0
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All datasetsMedConceptsQADTDFGVC-AircraftMini-ImageNet - 5-Shot LearningMini-Imagenet 5-way (1-shot)Stanford CarsMini-ImageNet - 1-Shot LearningPubMedQACaltech101CaseHOLDEuroSATFlowers-102

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1gpt-4-0125-previewAccuracy61.91—Unverified
2gpt-4-0125-previewAccuracy52.49—Unverified
3gpt-3.5-turboAccuracy41.48—Unverified
4gpt-3.5-turboAccuracy37.06—Unverified
5johnsnowlabs/JSL-MedMNX-7BAccuracy25.63—Unverified
6yikuan8/Clinical-LongformerAccuracy25.55—Unverified
7BioMistral/BioMistral-7B-DAREAccuracy25.06—Unverified
8yikuan8/Clinical-LongformerAccuracy25.04—Unverified
9PharMolix/BioMedGPT-LM-7BAccuracy24.92—Unverified
10PharMolix/BioMedGPT-LM-7BAccuracy24.75—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean67.27—Unverified
2SaSPA + CAL4-shot Accuracy48.3—Unverified
3Real-Guidance + CAL4-shot Accuracy41.5—Unverified
4CAL4-shot Accuracy40.9—Unverified
#ModelMetricClaimedVerifiedStatus
1SaSPA + CALHarmonic mean52.2—Unverified
2CALHarmonic mean35.2—Unverified
3Variational Prompt TuningHarmonic mean34.69—Unverified
4Real-Guidance + CALHarmonic mean34.5—Unverified
#ModelMetricClaimedVerifiedStatus
1BGNNAccuracy92.7—Unverified
2TIM-GDAccuracy87.4—Unverified
3UNEM-GaussianAccuracy66.4—Unverified
#ModelMetricClaimedVerifiedStatus
1EASY (transductive)Accuracy82.75—Unverified
2HCTransformers5 way 1~2 shot74.74—Unverified
3HyperShotAccuracy53.18—Unverified
#ModelMetricClaimedVerifiedStatus
1SaSPA + CAL4-shot Accuracy66.7—Unverified
2Real-Guidance + CAL4-shot Accuracy44.3—Unverified
3CAL4-shot Accuracy42.2—Unverified
#ModelMetricClaimedVerifiedStatus
1HCTransformersAcc74.74—Unverified
2DPGNAcc67.6—Unverified
#ModelMetricClaimedVerifiedStatus
1MetaGen Blended RAG (zero-shot)Accuracy77.9—Unverified
2CoT-T5-11B (1024 Shot)Accuracy73.42—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean96.44—Unverified
#ModelMetricClaimedVerifiedStatus
1CoT-T5-11B (1024 Shot)Accuracy68.3—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean77.71—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean81.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean91.57—Unverified
#ModelMetricClaimedVerifiedStatus
1CovidExpertAUC-ROC1—Unverified
#ModelMetricClaimedVerifiedStatus
1CoT-T5-11B (1024 Shot)Accuracy78.02—Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy65.7—Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy73.2—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean96.82—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean73.07—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean78.51—Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy52.3—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean79—Unverified