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 20012050 of 2964 papers

TitleStatusHype
FlipDA: Effective and Robust Data Augmentation for Few-Shot LearningCode1
Prototype Completion for Few-Shot LearningCode1
Boosting the Generalization Capability in Cross-Domain Few-shot Learning via Noise-enhanced Supervised AutoencoderCode0
Deep Metric Learning for Open World Semantic SegmentationCode1
Transductive Few-Shot Classification on the Oblique ManifoldCode0
Noisy Channel Language Model Prompting for Few-Shot Text ClassificationCode1
The Role of Global Labels in Few-Shot Classification and How to Infer Them0
Impact of Aliasing on Generalization in Deep Convolutional Networks0
Few-shot Unsupervised Domain Adaptation with Image-to-class Sparse Similarity Encoding0
Elaborative Rehearsal for Zero-shot Action RecognitionCode1
Uniform Sampling over Episode DifficultyCode0
From LSAT: The Progress and Challenges of Complex ReasoningCode1
Bayesian Active Meta-Learning for Few Pilot Demodulation and EqualizationCode0
Recurrent Mask Refinement for Few-Shot Medical Image SegmentationCode1
Meta-learning for Classifying Previously Unseen Data Source into Previously Unseen Emotional CategoriesCode0
Semi-supervised Meta-learning for Cross-domain Few-shot Intent Classification0
Meta Learning and Its Applications to Natural Language Processing0
UoB\_UK at SemEval 2021 Task 2: Zero-Shot and Few-Shot Learning for Multi-lingual and Cross-lingual Word Sense Disambiguation.0
Improving Social Meaning Detection with Pragmatic Masking and Surrogate Fine-TuningCode0
Bayesian Embeddings for Few-Shot Open World Recognition0
Few-Shot and Continual Learning with Attentive Independent MechanismsCode1
Automated Human Cell Classification in Sparse Datasets using Few-Shot Learning0
Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target DataCode1
A Transductive Maximum Margin Classifier for Few-Shot Learning0
Will Multi-modal Data Improves Few-shot Learning?0
ProtoTransformer: A Meta-Learning Approach to Providing Student FeedbackCode1
Modelling Latent Translations for Cross-Lingual TransferCode1
Class-Incremental Domain Adaptation with Smoothing and Calibration for Surgical Report GenerationCode1
External-Memory Networks for Low-Shot Learning of Targets in Forward-Looking-Sonar Imagery0
Trip-ROMA: Self-Supervised Learning with Triplets and Random MappingsCode0
Design of a Graphical User Interface for Few-Shot Machine Learning Classification of Electron Microscopy DataCode1
Few Shots Are All You Need: A Progressive Few Shot Learning Approach for Low Resource Handwritten Text RecognitionCode1
Boosting Few-Shot Classification with View-Learnable Contrastive LearningCode1
Rectifying the Shortcut Learning of Background for Few-Shot LearningCode1
Multi-Level Contrastive Learning for Few-Shot Problems0
Next-item Recommendations in Short Sessions0
Wordcraft: a Human-AI Collaborative Editor for Story Writing0
FLEX: Unifying Evaluation for Few-Shot NLPCode1
FewCLUE: A Chinese Few-shot Learning Evaluation BenchmarkCode1
Leveraging Hierarchical Structures for Few-Shot Musical Instrument RecognitionCode1
Sequential Recommendation for Cold-start Users with Meta Transitional LearningCode1
Few-shot Learning with Global Relatedness Decoupled-Distillation0
Learn from Anywhere: Rethinking Generalized Zero-Shot Learning with Limited Supervision0
Finding Significant Features for Few-Shot Learning using Dimensionality Reduction0
ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and GenerationCode0
Few-shot Learning for Unsupervised Feature Selection0
Cross-domain Few-shot Learning with Task-specific AdaptersCode1
Few-Shot Learning with a Strong TeacherCode1
SIMPL: Generating Synthetic Overhead Imagery to Address Zero-shot and Few-Shot Detection ProblemsCode0
Few-Shot Electronic Health Record Coding through Graph Contrastive LearningCode0
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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