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 11511200 of 2964 papers

TitleStatusHype
Simulated Annealing in Early Layers Leads to Better GeneralizationCode0
WikiGoldSK: Annotated Dataset, Baselines and Few-Shot Learning Experiments for Slovak Named Entity RecognitionCode0
Revisiting Automated Prompting: Are We Actually Doing Better?Code0
Learning to Learn with Indispensable Connections0
Meta-Learning with a Geometry-Adaptive PreconditionerCode1
Sociocultural knowledge is needed for selection of shots in hate speech detection tasks0
Not All Features Matter: Enhancing Few-shot CLIP with Adaptive Prior RefinementCode1
Improving Few-Shot Inductive Learning on Temporal Knowledge Graphs using Confidence-Augmented Reinforcement LearningCode0
A Closer Look at Few-Shot 3D Point Cloud ClassificationCode1
Cross-Cultural Transfer Learning for Chinese Offensive Language Detection0
What Makes for Effective Few-shot Point Cloud Classification?Code1
Point2Vec for Self-Supervised Representation Learning on Point CloudsCode1
Improving Large Language Models for Clinical Named Entity Recognition via Prompt EngineeringCode1
Channel Phase Processing in Wireless Networks for Human Activity Recognition0
On Codex Prompt Engineering for OCL Generation: An Empirical Study0
Few-Shot 3D Point Cloud Semantic Segmentation via Stratified Class-Specific Attention Based Transformer Network0
Learning Expressive Prompting With Residuals for Vision Transformers0
Generalizable Denoising of Microscopy Images using Generative Adversarial Networks and Contrastive Learning0
Universal Few-shot Learning of Dense Prediction Tasks with Visual Token MatchingCode2
Dealing With Heterogeneous 3D MR Knee Images: A Federated Few-Shot Learning Method With Dual Knowledge DistillationCode0
Supervised Masked Knowledge Distillation for Few-Shot TransformersCode1
Semantic Prompt for Few-Shot Image RecognitionCode1
SPEC: Summary Preference Decomposition for Low-Resource Abstractive Summarization0
Few Shot Medical Image Segmentation with Cross Attention TransformerCode1
ReVersion: Diffusion-Based Relation Inversion from ImagesCode2
Self-supervised Meta-Prompt Learning with Meta-Gradient Regularization for Few-shot GeneralizationCode0
Decomposed Prototype Learning for Few-Shot Scene Graph Generation0
A general-purpose AI assistant embedded in an open-source radiology information system0
Remote Task-oriented Grasp Area Teaching By Non-Experts through Interactive Segmentation and Few-Shot LearningCode0
Instance-Conditioned GAN Data Augmentation for Representation Learning0
Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-shot Learning with Hyperspherical EmbeddingsCode1
A Survey of Deep Visual Cross-Domain Few-Shot Learning0
SpatialFormer: Semantic and Target Aware Attentions for Few-Shot LearningCode0
Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey0
GPT-4 Technical ReportCode6
HazardNet: Road Debris Detection by Augmentation of Synthetic Models0
Meta-learning approaches for few-shot learning: A survey of recent advances0
Gradient-Regulated Meta-Prompt Learning for Generalizable Vision-Language Models0
DETA: Denoised Task Adaptation for Few-Shot LearningCode1
Consistency Analysis of ChatGPT0
MaskDiff: Modeling Mask Distribution with Diffusion Probabilistic Model for Few-Shot Instance SegmentationCode0
Knowledge-augmented Few-shot Visual Relation Detection0
MenuCraft: Interactive Menu System Design with Large Language ModelsCode0
Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human LanguageCode1
Prismer: A Vision-Language Model with Multi-Task ExpertsCode1
Exploring Data Augmentation Methods on Social Media Corpora0
Prompt, Generate, then Cache: Cascade of Foundation Models makes Strong Few-shot LearnersCode2
A Few-Shot Attention Recurrent Residual U-Net for Crack SegmentationCode0
Mixture of Soft Prompts for Controllable Data GenerationCode0
Self-Supervised Few-Shot Learning for Ischemic Stroke Lesion SegmentationCode0
Show:102550
← PrevPage 24 of 60Next →

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