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 26512700 of 2964 papers

TitleStatusHype
Graph convolutional networks for learning with few clean and many noisy labelsCode0
Adversarially Robust Few-Shot Learning: A Meta-Learning ApproachCode0
GPT-3 Models are Poor Few-Shot Learners in the Biomedical DomainCode0
Rethinking Task Sampling for Few-shot Vision-Language Transfer LearningCode0
Robust Few-shot Learning Without Using any Adversarial SamplesCode0
Robust Fine-Tuning of Vision-Language Models for Domain GeneralizationCode0
Adaptive Prototypical NetworksCode0
Adaptive Posterior Learning: few-shot learning with a surprise-based memory moduleCode0
GPS: Genetic Prompt Search for Efficient Few-shot LearningCode0
Gotta Learn Fast: A New Benchmark for Generalization in RLCode0
Robust Meta-learning with Sampling Noise and Label Noise via Eigen-ReptileCode0
Robust Meta-Representation Learning via Global Label Inference and ClassificationCode0
Modular Adaptation for Cross-Domain Few-Shot LearningCode0
Modularized Networks for Few-shot Hateful Meme DetectionCode0
GOGGLES: Automatic Image Labeling with Affinity CodingCode0
Robust Prototypical Few-Shot Organ Segmentation with Regularized Neural-ODEsCode0
Motamot: A Dataset for Revealing the Supremacy of Large Language Models over Transformer Models in Bengali Political Sentiment AnalysisCode0
Global Convolutional Neural ProcessesCode0
Support-Set Context Matters for Bongard ProblemsCode0
BioRAGent: A Retrieval-Augmented Generation System for Showcasing Generative Query Expansion and Domain-Specific Search for Scientific Q&ACode0
Task Augmentation by Rotating for Meta-LearningCode0
Multidimensional Belief Quantification for Label-Efficient Meta-LearningCode0
Cross-domain Multi-modal Few-shot Object Detection via Rich TextCode0
A Bridge Between Hyperparameter Optimization and Learning-to-learnCode0
Transforming Scholarly Landscapes: Influence of Large Language Models on Academic Fields beyond Computer ScienceCode0
Adaptive Masking Enhances Visual GroundingCode0
AniWho : A Quick and Accurate Way to Classify Anime Character Faces in ImagesCode0
Multi-level Metric Learning for Few-shot Image RecognitionCode0
Multi-level Second-order Few-shot LearningCode0
Task-Embedded Control Networks for Few-Shot Imitation LearningCode0
Bi-Matching Mechanism to Combat the Long Tail of Word Sense DisambiguationCode0
Gestalt-Guided Image Understanding for Few-Shot LearningCode0
Massively Multilingual Transfer for NERCode0
Generative Transfer Learning: Covid-19 Classification with a few Chest X-ray ImagesCode0
Beyond Textual Constraints: Learning Novel Diffusion Conditions with Fewer ExamplesCode0
Adaptive Gradient-Based Meta-Learning MethodsCode0
Task-Specific Alignment and Multiple Level Transformer for Few-Shot Action RecognitionCode0
Multi-Modal Fusion by Meta-InitializationCode0
Generating Zero-shot Abstractive Explanations for Rumour VerificationCode0
Zero-Shot Stance Detection using Contextual Data Generation with LLMsCode0
Generating Classification Weights with GNN Denoising Autoencoders for Few-Shot LearningCode0
A New Dataset and Empirical Study for Sentence Simplification in ChineseCode0
Generate, Annotate, and Learn: NLP with Synthetic TextCode0
tax2vec: Constructing Interpretable Features from Taxonomies for Short Text ClassificationCode0
Scalable Few-Shot Learning of Robust Biomedical Name RepresentationsCode0
Beyond Scores: A Modular RAG-Based System for Automatic Short Answer Scoring with FeedbackCode0
Multi-Pretext Attention Network for Few-shot Learning with Self-supervisionCode0
Trip-ROMA: Self-Supervised Learning with Triplets and Random MappingsCode0
A Neural Topic-Attention Model for Medical Term Abbreviation DisambiguationCode0
Conditional Deep Gaussian Processes: multi-fidelity kernel 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