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
RW-Net: Enhancing Few-Shot Point Cloud Classification with a Wavelet Transform Projection-based Network—0
SAFLEX: Self-Adaptive Augmentation via Feature Label Extrapolation—0
SAFT: Towards Out-of-Distribution Generalization in Fine-Tuning—0
SAGE: Saliency-Guided Mixup with Optimal Rearrangements—0
SAMIC: Segment Anything with In-Context Spatial Prompt Engineering—0
SAM-IF: Leveraging SAM for Incremental Few-Shot Instance Segmentation—0
SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting—0
SampleLLM: Optimizing Tabular Data Synthesis in Recommendations—0
SAR-NeRF: Neural Radiance Fields for Synthetic Aperture Radar Multi-View Representation—0
SaSi: A Self-augmented and Self-interpreted Deep Learning Approach for Few-shot Cryo-ET Particle Detection—0
SB-MTL: Score-based Meta Transfer-Learning for Cross-Domain Few-Shot Learning—0
Scalable Prompt Generation for Semi-supervised Learning with Language Models—0
Scaling ASR Improves Zero and Few Shot Learning—0
Scaling Laws for the Few-Shot Adaptation of Pre-trained Image Classifiers—0
Scaling Vision Transformers—0
Score Neural Operator: A Generative Model for Learning and Generalizing Across Multiple Probability Distributions—0
SD-MAD: Sign-Driven Few-shot Multi-Anomaly Detection in Medical Images—0
Search-based Optimisation of LLM Learning Shots for Story Point Estimation—0
Second-order contexts from lexical substitutes for few-shot learning of word representations—0
SegICL: A Multimodal In-context Learning Framework for Enhanced Segmentation in Medical Imaging—0
Segmentation of Knee Bones for Osteoarthritis Assessment: A Comparative Analysis of Supervised, Few-Shot, and Zero-Shot Learning Approaches—0
Segmenting Fetal Head with Efficient Fine-tuning Strategies in Low-resource Settings: an empirical study with U-Net—0
Selecting Between BERT and GPT for Text Classification in Political Science Research—0
Selecting Shots for Demographic Fairness in Few-Shot Learning with Large Language Models—0
Selecting task with optimal transport self-supervised learning for few-shot classification—0
Self-Attentive Constituency Parsing for UCCA-based Semantic Parsing—0
Self-Augmentation: Generalizing Deep Networks to Unseen Classes for Few-Shot Learning—0
Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning—0
Self-Denoising Neural Networks for Few Shot Learning—0
Self Paced Adversarial Training for Multimodal Few-shot Learning—0
Self-Refined Generative Foundation Models for Wireless Traffic Prediction—0
Self-supervised Adaptive Pre-training of Multilingual Speech Models for Language and Dialect Identification—0
Self-Supervised and Few-Shot Learning for Robust Bioaerosol Monitoring—0
Self-supervised Contrastive Zero to Few-shot Learning from Small, Long-tailed Text data—0
Efficient Personalized Speech Enhancement through Self-Supervised Learning—0
Self-supervised Pretraining for Decision Foundation Model: Formulation, Pipeline and Challenges—0
Self-Supervised Prime-Dual Networks for Few-Shot Image Classification—0
Self-Supervised Representation Learning for CAD—0
Self-mentoring: a new deep learning pipeline to train a self-supervised U-net for few-shot learning of bio-artificial capsule segmentation—0
Self-Supervised WiFi-Based Activity Recognition—0
Self-Taught Cross-Domain Few-Shot Learning with Weakly Supervised Object Localization and Task-Decomposition—0
Self-Training with Improved Regularization for Sample-Efficient Chest X-Ray Classification—0
Self-Transriber: Few-shot Lyrics Transcription with Self-training—0
Semantically Proportional Patchmix for Few-Shot Learning—0
Semantic-Based Few-Shot Learning by Interactive Psychometric Testing—0
SEM-CLIP: Precise Few-Shot Learning for Nanoscale Defect Detection in Scanning Electron Microscope Image—0
Semi Few-Shot Attribute Translation—0
Semi-Supervised and Active Few-Shot Learning with Prototypical Networks—0
Semi-supervised Contrastive Learning with Similarity Co-calibration—0
Semi-Supervised Few-Shot Classification with Deep Invertible Hybrid Models—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