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 801850 of 2964 papers

TitleStatusHype
A Survey on Stability of Learning with Limited Labelled Data and its Sensitivity to the Effects of Randomness0
Generalized Robot 3D Vision-Language Model with Fast Rendering and Pre-Training Vision-Language AlignmentCode3
Applying Large Language Models and Chain-of-Thought for Automatic Scoring0
Simple Semantic-Aided Few-Shot LearningCode1
Clinical Risk Prediction Using Language Models: Benefits And Considerations0
Explaining CLIP's performance disparities on data from blind/low vision users0
A transductive few-shot learning approach for classification of digital histopathological slides from liver cancer0
Robust Transductive Few-shot Learning via Joint Message Passing and Prototype-based Soft-label Propagation0
ClimateX: Do LLMs Accurately Assess Human Expert Confidence in Climate Statements?Code0
When the Few Outweigh the Many: Illicit Content Recognition with Few-Shot Learning0
Target-Free Compound Activity Prediction via Few-Shot Learning0
Improving Denoising Diffusion Probabilistic Models via Exploiting Shared Representations0
CaesarNeRF: Calibrated Semantic Representation for Few-shot Generalizable Neural RenderingCode1
Italian Crossword Generator: Enhancing Education through Interactive Word Puzzles0
Deficiency of Large Language Models in Finance: An Empirical Examination of Hallucination0
MEDITRON-70B: Scaling Medical Pretraining for Large Language ModelsCode4
Learning to Skip for Language Modeling0
ID-like Prompt Learning for Few-Shot Out-of-Distribution DetectionCode1
Understanding the Role of Textual Prompts in LLM for Time Series Forecasting: an Adapter ViewCode1
Inferring Latent Class Statistics from Text for Robust Visual Few-Shot LearningCode0
A Multi-solution Study on GDPR AI-enabled Completeness Checking of DPAs0
Language-guided Few-shot Semantic Segmentation0
Benchmarking Toxic Molecule Classification using Graph Neural Networks and Few Shot Learning0
Descriptor and Word Soups: Overcoming the Parameter Efficiency Accuracy Tradeoff for Out-of-Distribution Few-shot LearningCode0
NERIF: GPT-4V for Automatic Scoring of Drawn Models0
Modeling Political Orientation of Social Media Posts: An Extended Analysis0
Exploring Graph Classification Techniques Under Low Data Constraints: A Comprehensive Study0
Optimal Strategies to Perform Multilingual Analysis of Social Content for a Novel Dataset in the Tourism Domain0
Generalization of Fitness Exercise Recognition from Doppler Measurements by Domain-adaption and Few-Shot Learning0
Using Guided Transfer Learning to Predispose AI Agent to Learn Efficiently from Small RNA-sequencing Datasets0
Point Cloud Self-supervised Learning via 3D to Multi-view Masked AutoencoderCode1
A Language Agent for Autonomous DrivingCode0
Tabular Few-Shot Generalization Across Heterogeneous Feature Spaces0
CLEAN-EVAL: Clean Evaluation on Contaminated Large Language Models0
Dual-channel Prototype Network for few-shot Classification of Pathological Images0
Selecting Shots for Demographic Fairness in Few-Shot Learning with Large Language Models0
Cross-dataset domain adaptation for the classification COVID-19 using chest computed tomography images0
In-context Learning and Gradient Descent RevisitedCode0
Few Shot Learning for the Classification of Confocal Laser Endomicroscopy Images of Head and Neck Tumors0
Learning Knowledge-Enhanced Contextual Language Representations for Domain Natural Language Understanding0
Relation Extraction in underexplored biomedical domains: A diversity-optimised sampling and synthetic data generation approachCode0
Chatbots Are Not Reliable Text AnnotatorsCode0
Enhancing Instance-Level Image Classification with Set-Level Labels0
Analysis and Applications of Deep Learning with Finite Samples in Full Life-Cycle Intelligence of Nuclear Power Generation0
Multilingual Mathematical AutoformalizationCode1
Meta-Adapter: An Online Few-shot Learner for Vision-Language ModelCode1
Few-shot Learning using Data Augmentation and Time-Frequency Transformation for Time Series Classification0
Nexus at ArAIEval Shared Task: Fine-Tuning Arabic Language Models for Propaganda and Disinformation Detection0
Robust Fine-Tuning of Vision-Language Models for Domain GeneralizationCode0
Multi-level Relation Learning for Cross-domain Few-shot Hyperspectral Image ClassificationCode0
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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