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 201250 of 2964 papers

TitleStatusHype
Adversarial Feature Augmentation for Cross-domain Few-shot ClassificationCode1
Enhancing Few-shot Image Classification with Cosine TransformerCode1
Adversarial Feature Hallucination Networks for Few-Shot LearningCode1
ESPT: A Self-Supervised Episodic Spatial Pretext Task for Improving Few-Shot LearningCode1
Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With SupervoxelsCode1
Anomaly Detection of Defect using Energy of Point Pattern Features within Random Finite Set FrameworkCode1
DC-SAM: In-Context Segment Anything in Images and Videos via Dual ConsistencyCode1
Attentive Weights Generation for Few Shot Learning via Information MaximizationCode1
Expanding Event Modality Applications through a Robust CLIP-Based EncoderCode1
Explanation-Guided Training for Cross-Domain Few-Shot ClassificationCode1
An Overview of Deep Learning Architectures in Few-Shot Learning DomainCode1
Anti-aliasing Semantic Reconstruction for Few-Shot Semantic SegmentationCode1
RARR: Researching and Revising What Language Models Say, Using Language ModelsCode1
Exploring Foundation Models Fine-Tuning for Cytology ClassificationCode1
Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive ProcessesCode1
Fast Learning of Dynamic Hand Gesture Recognition with Few-Shot Learning ModelsCode1
Feature Generation for Long-tail ClassificationCode1
Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language ModelsCode1
A Prompt Learning Framework for Source Code SummarizationCode1
AdaptKeyBERT: An Attention-Based approach towards Few-Shot & Zero-Shot Domain Adaptation of KeyBERTCode1
D^2ST-Adapter: Disentangled-and-Deformable Spatio-Temporal Adapter for Few-shot Action RecognitionCode1
FewCLUE: A Chinese Few-shot Learning Evaluation BenchmarkCode1
A Rationale-Centric Framework for Human-in-the-loop Machine LearningCode1
A Few-shot Learning Approach for Historical Ciphered Manuscript RecognitionCode1
ChemEval: A Comprehensive Multi-Level Chemical Evaluation for Large Language ModelsCode1
Few-Shot Bot: Prompt-Based Learning for Dialogue SystemsCode1
Few-Shot Diffusion ModelsCode1
Few-Shot Document-Level Relation ExtractionCode1
Few-shot Learner Parameterization by Diffusion Time-stepsCode1
Few-Shot Learning by Dimensionality Reduction in Gradient SpaceCode1
Few-shot Decoding of Brain Activation MapsCode1
Few Shot Learning Framework to Reduce Inter-observer Variability in Medical ImagesCode1
DeIL: Direct-and-Inverse CLIP for Open-World Few-Shot LearningCode1
Artistic Glyph Image Synthesis via One-Stage Few-Shot LearningCode1
A Modern Self-Referential Weight Matrix That Learns to Modify ItselfCode1
Few-shot Learning with Class-Covariance Metric for Hyperspectral Image ClassificationCode1
Few-shot Learning with LSSVM Base Learner and Transductive ModulesCode1
Few-shot Learning with Multilingual Language ModelsCode1
A Simple Exponential Family Framework for Zero-Shot LearningCode1
A Closer Look at Few-Shot 3D Point Cloud ClassificationCode1
Few-Shot Medical Image Segmentation via a Region-enhanced Prototypical TransformerCode1
AskIt: Unified Programming Interface for Programming with Large Language ModelsCode1
Cross-domain Few-shot Object Detection with Multi-modal Textual EnrichmentCode1
Few-Shot Named Entity Recognition: A Comprehensive StudyCode1
Few-shot Network Anomaly Detection via Cross-network Meta-learningCode1
Few-shot Object Detection via Feature ReweightingCode1
A Closer Look at Few-shot ClassificationCode1
FewshotQA: A simple framework for few-shot learning of question answering tasks using pre-trained text-to-text modelsCode1
CrossFi: A Cross Domain Wi-Fi Sensing Framework Based on Siamese NetworkCode1
COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approachCode1
Show:102550
← PrevPage 5 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