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 2101–2150 of 2964 papers

TitleStatusHype
Prototypical Verbalizer for Prompt-based Few-shot Tuning—0
POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection—0
Metaphor Detection for Low Resource Languages: From Zero-Shot to Few-Shot Learning in Middle High German—0
NSP-NER: A Prompt-based Learner for Few-shot NER Driven by Next Sentence Prediction—0
Towards Unified Prompt Tuning for Few-shot Learning—0
Few-shot Named Entity Recognition with Joint Token and Sentence Awareness—0
UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis—0
Meta-Adapter: Parameter Efficient Few-Shot Learning through Meta-Learning—0
Can Explanations Be Useful for Calibrating Black Box Models?—0
Learning to Learn Recognising Biomedical Entities from Multiple Domains with Task Hardness—0
Building a Role Specified Open-Domain Dialogue System Leveraging Large-Scale Language Models—0
Inverse is Better! Fast and Accurate Prompt for Slot Tagging—0
A Self-Adaptive Learning Rate and Curriculum Learning Based Framework for Few-Shot Text Classification—0
Making Small Language Models Better Few-Shot Learners—0
On the Multilingual Capabilities of Very Large-Scale English Language Models—0
Prompt-Guided Few-Shot Event Detection—0
Empirical Evaluation of Topic Zero- and Few-Shot Learning for Stance Dissonance Detection—0
Bi-Matching Mechanism to Combat the Long Tail of Word Sense DisambiguationCode0
Cross-domain Named Entity Recognition via Graph Matching—0
Few-Shot Knowledge Graph Completion with Data Fusion and Augmentation—0
CoLLIE: Continual Learning of Language Grounding from Language-Image EmbeddingsCode0
Scaling ASR Improves Zero and Few Shot Learning—0
Enhancing Prototypical Few-Shot Learning by Leveraging the Local-Level Strategy—0
Crowdsourcing with Meta-Workers: A New Way to Save the Budget—0
Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLP—0
A MIMO Radar-Based Metric Learning Approach for Activity Recognition—0
Continual Few-Shot Learning for Text ClassificationCode0
Few-shot learning with improved local representations via bias rectify module—0
Data-Efficient Language Shaped Few-shot Image ClassificationCode0
Learning Prototype Representations Across Few-Shot Tasks for Event DetectionCode0
Few-Shot Named Entity Recognition: An Empirical Baseline Study—0
Influential Prototypical Networks for Few Shot Learning: A Dermatological Case Study—0
MFNet: Multi-class Few-shot Segmentation Network with Pixel-wise Metric Learning—0
Domain Agnostic Few-Shot Learning For Document Intelligence—0
Self-Denoising Neural Networks for Few Shot Learning—0
Task-Aware Meta Learning-based Siamese Neural Network for Classifying Obfuscated Malware—0
Meta-Learning for Multi-Label Few-Shot Classification—0
Instant Response Few-shot Object Detection with Meta Strategy and Explicit Localization InferenceCode0
Simultaneous Perturbation Method for Multi-Task Weight Optimization in One-Shot Meta-LearningCode0
GCCN: Global Context Convolutional Network—0
A Strong Baseline for Semi-Supervised Incremental Few-Shot Learning—0
Contextual Gradient Scaling for Few-Shot LearningCode0
On Label-Efficient Computer Vision: Building Fast and Effective Few-Shot Image Classifiers—0
Ortho-Shot: Low Displacement Rank Regularization with Data Augmentation for Few-Shot Learning—0
HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model CompressionCode0
Few-Shot Learning with Siamese Networks and Label Tuning—0
A MIMO Radar-based Few-Shot Learning Approach for Human-ID—0
Making Pre-trained Language Models End-to-end Few-shot Learners with Contrastive Prompt Tuning—0
PPT: Pre-trained Prompt Tuning for Few-shot Learning—0
Omni-Training: Bridging Pre-Training and Meta-Training for Few-Shot Learning—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