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 18011850 of 2964 papers

TitleStatusHype
An evaluation of GPT models for phenotype concept recognition0
A New Method for Features Normalization in Motor Imagery Few-Shot Learning using Resting-State0
Exploring In-Context Learning of Textless Speech Language Model for Speech Classification Tasks0
AnnotatedTables: A Large Tabular Dataset with Language Model Annotations0
Annotation-Efficient Untrimmed Video Action Recognition0
Anomaly Crossing: New Horizons for Video Anomaly Detection as Cross-domain Few-shot Learning0
A Novel Compact LLM Framework for Local, High-Privacy EHR Data Applications0
AnyTrans: Translate AnyText in the Image with Large Scale Models0
Applying Fine-Tuned LLMs for Reducing Data Needs in Load Profile Analysis0
Applying Large Language Models and Chain-of-Thought for Automatic Scoring0
Approximating Human-Like Few-shot Learning with GPT-based Compression0
A Practical Guide to Fine-tuning Language Models with Limited Data0
A Prompt Refinement-based Large Language Model for Metro Passenger Flow Forecasting under Delay Conditions0
AraMUS: Pushing the Limits of Data and Model Scale for Arabic Natural Language Processing0
Are Fewer Labels Possible for Few-shot Learning?0
Are Few-shot Learning Benchmarks Too Simple ?0
A Reinforcement Learning-based Offensive semantics Censorship System for Chatbots0
Are Large Language Models Good Essay Graders?0
A Revision of Neural Tangent Kernel-based Approaches for Neural Networks0
A Nested Bi-level Optimization Framework for Robust Few Shot Learning0
Argumentative Stance Prediction: An Exploratory Study on Multimodality and Few-Shot Learning0
A Self-Adaptive Learning Rate and Curriculum Learning Based Framework for Few-Shot Text Classification0
A Similarity Paradigm Through Textual Regularization Without Forgetting0
A Simple Task-aware Contrastive Local Descriptor Selection Strategy for Few-shot Learning between inter class and intra class0
Supervised Graph Contrastive Learning for Few-shot Node Classification0
Aspect-Based Few-Shot Learning0
Assertion Enhanced Few-Shot Learning: Instructive Technique for Large Language Models to Generate Educational Explanations0
Assessing the Performance of the DINOv2 Self-supervised Learning Vision Transformer Model for the Segmentation of the Left Atrium from MRI Images0
Assessing two novel distance-based loss functions for few-shot image classification0
Associative Adversarial Learning Based on Selective Attack0
Assume, Augment and Learn: Unsupervised Few-Shot Meta-Learning via Random Labels and Data Augmentation0
A Stochastic Approach to Bi-Level Optimization for Hyperparameter Optimization and Meta Learning0
A Strong Baseline for Semi-Supervised Incremental Few-Shot Learning0
A Study of Few-Shot Audio Classification0
A Study on Prompt-based Few-Shot Learning Methods for Belief State Tracking in Task-oriented Dialog Systems0
A Study on Representation Transfer for Few-Shot Learning0
A Survey of Deep Learning for Low-Shot Object Detection0
A Survey of Deep Visual Cross-Domain Few-Shot Learning0
A Survey of Few-Shot Learning for Biomedical Time Series0
A Survey of Learning on Small Data: Generalization, Optimization, and Challenge0
A Survey on Few-Shot Class-Incremental Learning0
A Survey On Few-shot Knowledge Graph Completion with Structural and Commonsense Knowledge0
A Few-Shot Learning Focused Survey on Recent Named Entity Recognition and Relation Classification Methods0
Asymmetric Distribution Measure for Few-shot Learning0
A Systematic Review of Few-Shot Learning in Medical Imaging0
A Theoretical Analysis of the Number of Shots in Few-Shot Learning0
A theoretically grounded characterization of feature representations0
A Theory of Human-Like Few-Shot Learning0
A Theory of Self-Supervised Framework for Few-Shot Learning0
A transductive few-shot learning approach for classification of digital histopathological slides from liver cancer0
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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