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 2001–2050 of 2964 papers

TitleStatusHype
Contrastive Graph Few-Shot Learning—0
Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification—0
Contrastive Prototype Learning with Augmented Embeddings for Few-Shot Learning—0
Conversation Style Transfer using Few-Shot Learning—0
ConvoGen: Enhancing Conversational AI with Synthetic Data: A Multi-Agent Approach—0
Convolutional Ensembling based Few-Shot Defect Detection Technique—0
COOL: Comprehensive Knowledge Enhanced Prompt Learning for Domain Adaptive Few-shot Fake News Detection—0
CoRAG: Collaborative Retrieval-Augmented Generation—0
Core Knowledge Learning Framework for Graph Adaptation and Scalability Learning—0
Correlation Weighted Prototype-based Self-Supervised One-Shot Segmentation of Medical Images—0
COSTA: Co-Occurrence Statistics for Zero-Shot Classification—0
COVID-19 Surveillance through Twitter using Self-Supervised and Few Shot Learning—0
CovidExpert: A Triplet Siamese Neural Network framework for the detection of COVID-19—0
CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis—0
CRoF: CLIP-based Robust Few-shot Learning on Noisy Labels—0
CrossCodeBench: Benchmarking Cross-Task Generalization of Source Code Models—0
Cross-Cultural Transfer Learning for Chinese Offensive Language Detection—0
Cross-dataset domain adaptation for the classification COVID-19 using chest computed tomography images—0
Cross-Domain Evaluation of Few-Shot Classification Models: Natural Images vs. Histopathological Images—0
Cross Domain Few-Shot Learning via Meta Adversarial Training—0
Cross-domain few-shot learning with unlabelled data—0
Cross-Domain Few-Shot Learning with Meta Fine-Tuning—0
Cross-Domain Few-Shot Relation Extraction via Representation Learning and Domain Adaptation—0
Cross-domain Named Entity Recognition via Graph Matching—0
Cross-heterogeneity Graph Few-shot Learning—0
Cross-Lingual Transfer Learning for Complex Word Identification—0
Cross-lingual Zero- and Few-shot Hate Speech Detection Utilising Frozen Transformer Language Models and AXEL—0
Cross-Linked Variational Autoencoders for Generalized Zero-Shot Learning—0
Cross-Modal Concept Learning and Inference for Vision-Language Models—0
Cross-Modal Few-Shot Learning: a Generative Transfer Learning Framework—0
Cross-Modal Few-Shot Learning with Second-Order Neural Ordinary Differential Equations—0
Cross-Modality Multi-Atlas Segmentation Using Deep Neural Networks—0
cross-modal knowledge enhancement mechanism for few-shot learning—0
Cross-Modal Learning for Chemistry Property Prediction: Large Language Models Meet Graph Machine Learning—0
Cross-Modal Mapping: Mitigating the Modality Gap for Few-Shot Image Classification—0
Cross-Modulation Networks for Few-Shot Learning—0
Few-shot Medical Image Segmentation via Cross-Reference Transformer—0
Crowdsourcing with Meta-Workers: A New Way to Save the Budget—0
CryoMAE: Few-Shot Cryo-EM Particle Picking with Masked Autoencoders—0
GCT: Graph Co-Training for Semi-Supervised Few-Shot Learning—0
CS/NLP at SemEval-2022 Task 4: Effective Data Augmentation Methods for Patronizing Language Detection and Multi-label Classification with RoBERTa and GPT3—0
Curvature: A signature for Action Recognition in Video Sequences—0
Curvature Generation in Curved Spaces for Few-Shot Learning—0
Customize Your Own Paired Data via Few-shot Way—0
Cut out the annotator, keep the cutout: better segmentation with weak supervision—0
CVOCSemRPL: Class-Variance Optimized Clustering, Semantic Information Injection and Restricted Pseudo Labeling based Improved Semi-Supervised Few-Shot Learning—0
DA-FDFtNet: Dual Attention Fake Detection Fine-tuning Network to Detect Various AI-Generated Fake Images—0
DASECount: Domain-Agnostic Sample-Efficient Wireless Indoor Crowd Counting via Few-shot Learning—0
Data-Efficient Goal-Oriented Conversation with Dialogue Knowledge Transfer Networks—0
Dataset Bias in Few-shot Image Recognition—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