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 651700 of 2964 papers

TitleStatusHype
Feature Generation for Long-tail ClassificationCode1
Look Closer to Supervise Better: One-Shot Font Generation via Component-Based DiscriminatorCode1
Make Prompt-based Black-Box Tuning Colorful: Boosting Model Generalization from Three Orthogonal PerspectivesCode1
Bayesian Meta-Learning for the Few-Shot Setting via Deep KernelsCode1
Exploring Efficient Few-shot Adaptation for Vision TransformersCode1
Easter2.0: Improving convolutional models for handwritten text recognitionCode1
Concept Learners for Few-Shot LearningCode1
Exploring Foundation Models Fine-Tuning for Cytology ClassificationCode1
Deeply Coupled Cross-Modal Prompt LearningCode1
MatchSeg: Towards Better Segmentation via Reference Image MatchingCode1
Deep Metric Learning for Open World Semantic SegmentationCode1
BECLR: Batch Enhanced Contrastive Few-Shot LearningCode1
EASE: Unsupervised Discriminant Subspace Learning for Transductive Few-Shot LearningCode1
Elaborative Rehearsal for Zero-shot Action RecognitionCode1
Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image ClassificationCode1
MetaAudio: A Few-Shot Audio Classification BenchmarkCode1
Defining Benchmarks for Continual Few-Shot LearningCode1
Deformation-Recovery Diffusion Model (DRDM): Instance Deformation for Image Manipulation and SynthesisCode1
Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target DataCode1
DeIL: Direct-and-Inverse CLIP for Open-World Few-Shot LearningCode1
Discrete and Soft Prompting for Multilingual ModelsCode1
EEG-Reptile: An Automatized Reptile-Based Meta-Learning Library for BCIsCode1
Meta Learning to Bridge Vision and Language Models for Multimodal Few-Shot LearningCode1
Meta-Learning via Classifier(-free) Diffusion GuidanceCode1
Better Few-Shot Relation Extraction with Label Prompt DropoutCode1
Meta-learning with differentiable closed-form solversCode1
Better Generalized Few-Shot Learning Even Without Base DataCode1
DenoiSeg: Joint Denoising and SegmentationCode1
MetaModulation: Learning Variational Feature Hierarchies for Few-Shot Learning with Fewer TasksCode1
MetaNODE: Prototype Optimization as a Neural ODE for Few-Shot LearningCode1
MetaScript: Few-Shot Handwritten Chinese Content Generation via Generative Adversarial NetworksCode1
DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuningCode1
Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot LearningCode1
Meta-Transfer Learning for Few-Shot LearningCode1
Extending Context Window of Large Language Models via Semantic CompressionCode1
Design of a Graphical User Interface for Few-Shot Machine Learning Classification of Electron Microscopy DataCode1
DETA: Denoised Task Adaptation for Few-Shot LearningCode1
Model-Agnostic Few-Shot Open-Set RecognitionCode1
Few-Shot Class-Incremental Learning via Training-Free Prototype CalibrationCode1
Detecting Hate Speech with GPT-3Code1
MSAGPT: Neural Prompting Protein Structure Prediction via MSA Generative Pre-TrainingCode1
DETReg: Unsupervised Pretraining with Region Priors for Object DetectionCode1
Few Shot Medical Image Segmentation with Cross Attention TransformerCode1
GPr-Net: Geometric Prototypical Network for Point Cloud Few-Shot LearningCode1
LFPT5: A Unified Framework for Lifelong Few-shot Language Learning Based on Prompt Tuning of T5Code1
Dialogue for Prompting: a Policy-Gradient-Based Discrete Prompt Generation for Few-shot LearningCode1
Multistage Relation Network With Dual-Metric for Few-Shot Hyperspectral Image ClassificationCode1
Multitask Pre-training of Modular Prompt for Chinese Few-Shot LearningCode1
DiffCLIP: Few-shot Language-driven Multimodal ClassifierCode1
POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance DetectionCode1
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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