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 19512000 of 2964 papers

TitleStatusHype
One-Class Meta-Learning: Towards Generalizable Few-Shot Open-Set Classification0
Exploring Prompt-based Few-shot Learning for Grounded Dialog Generation0
STraTA: Self-Training with Task Augmentation for Better Few-shot LearningCode0
Meta Navigator: Search for a Good Adaptation Policy for Few-shot Learning0
Fine-Grained Few Shot Learning with Foreground Object Transformation0
Online Unsupervised Learning of Visual Representations and CategoriesCode0
Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Framework0
Prior Omission of Dissimilar Source Domain(s) for Cost-Effective Few-Shot Learning0
LibFewShot: A Comprehensive Library for Few-shot LearningCode2
CINS: Comprehensive Instruction for Few-shot Learning in Task-oriented Dialog Systems0
Rapid Model Architecture Adaption for Meta-Learning0
What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained TransformersCode2
Bootstrapped Meta-LearningCode0
PPT: Pre-trained Prompt Tuning for Few-shot LearningCode1
Compositional Clustering: Applications to Multi-Label Object Recognition and Speaker IdentificationCode0
MapRE: An Effective Semantic Mapping Approach for Low-resource Relation Extraction0
Learning Opinion Summarizers by Selecting Informative ReviewsCode1
Adaptive Few-Shot Learning PoC Ultrasound COVID-19 Diagnostic System0
Discrete and Soft Prompting for Multilingual ModelsCode1
Few-shot Learning in Emotion Recognition of Spontaneous Speech Using a Siamese Neural Network with Adaptive Sample Pair Formation0
Few-shot Learning via Dependency Maximization and Instance Discriminant Analysis0
GPT-3 Models are Poor Few-Shot Learners in the Biomedical DomainCode0
Nearest Neighbour Few-Shot Learning for Cross-lingual ClassificationCode0
FewshotQA: A simple framework for few-shot learning of question answering tasks using pre-trained text-to-text modelsCode1
CAM-loss: Towards Learning Spatially Discriminative Feature Representations0
Self-Taught Cross-Domain Few-Shot Learning with Weakly Supervised Object Localization and Task-Decomposition0
Information Symmetry Matters: A Modal-Alternating Propagation Network for Few-Shot Learning0
ConQX: Semantic Expansion of Spoken Queries for Intent Detection based on Conditioned Text Generation0
Global Convolutional Neural ProcessesCode0
Do Prompt-Based Models Really Understand the Meaning of their Prompts?Code0
Probabilistic Ensembles of Zero- and Few-Shot Learning Models for Emotion Classification0
LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable PromptingCode0
Robust Retrieval Augmented Generation for Zero-shot Slot FillingCode1
On the Multilingual Capabilities of Very Large-Scale English Language ModelsCode0
Semi-Supervised Exaggeration Detection of Health Science Press ReleasesCode1
Want To Reduce Labeling Cost? GPT-3 Can HelpCode1
Self-training Improves Pre-training for Few-shot Learning in Task-oriented Dialog SystemsCode0
Binocular Mutual Learning for Improving Few-shot ClassificationCode1
Anomaly Detection of Defect using Energy of Point Pattern Features within Random Finite Set FrameworkCode1
MCML: A Novel Memory-based Contrastive Meta-Learning Method for Few Shot Slot Tagging0
Deep few-shot learning for bi-temporal building change detection0
Learning Class-level Prototypes for Few-shot Learning0
FEDI: Few-shot learning based on Earth Mover's Distance algorithm combined with deep residual network to identify diabetic retinopathyCode0
Few Shot Activity Recognition Using Variational Inference0
Neural TMDlayer: Modeling Instantaneous flow of features via SDE GeneratorsCode0
Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identificationCode1
AdapterHub Playground: Simple and Flexible Few-Shot Learning with AdaptersCode1
Generalized and Incremental Few-Shot Learning by Explicit Learning and Calibration without ForgettingCode1
Few-Shot Batch Incremental Road Object Detection via Detector Fusion0
Program Synthesis with Large Language ModelsCode1
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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