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 2751–2800 of 2964 papers

TitleStatusHype
Metacognition-Enhanced Few-Shot Prompting With Positive Reinforcement—0
MetaConcept: Learn to Abstract via Concept Graph for Weakly-Supervised Few-Shot Learning—0
Meta Cyclical Annealing Schedule: A Simple Approach to Avoiding Meta-Amortization Error—0
MetAdapt: Meta-Learned Task-Adaptive Architecture for Few-Shot Classification—0
MetaDiff: Meta-Learning with Conditional Diffusion for Few-Shot Learning—0
Meta-DM: Applications of Diffusion Models on Few-Shot Learning—0
Meta-DRN: Meta-Learning for 1-Shot Image Segmentation—0
MetaDT: Meta Decision Tree with Class Hierarchy for Interpretable Few-Shot Learning—0
Meta-Exploiting Frequency Prior for Cross-Domain Few-Shot Learning—0
Meta-free few-shot learning via representation learning with weight averaging—0
MetaGAN: An Adversarial Approach to Few-Shot Learning—0
MetaHistoSeg: A Python Framework for Meta Learning in Histopathology Image Segmentation—0
MetaICL: Learning to Learn In Context—0
Meta In-Context Learning Makes Large Language Models Better Zero and Few-Shot Relation Extractors—0
Meta-Learned Confidence for Transductive Few-shot Learning—0
Meta-Learner with Linear Nulling—0
Meta-Learning across Meta-Tasks for Few-Shot Learning—0
Meta Learning and Its Applications to Natural Language Processing—0
Meta-learning approaches for few-shot learning: A survey of recent advances—0
Meta-Learning by Hallucinating Useful Examples—0
Meta-Learning for Multi-Label Few-Shot Classification—0
Meta-Learning Divergences of Variational Inference—0
Meta-Learning GNN Initializations for Low-Resource Molecular Property Prediction—0
Meta-learning in healthcare: A survey—0
Meta-Learning Neural Procedural Biases—0
Meta-Learning of NAS for Few-shot Learning in Medical Image Applications—0
Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection—0
Meta-Learning to Detect Rare Objects—0
Meta-Learning with Domain Adaptation for Few-Shot Learning under Domain Shift—0
Meta Learning with Minimax Regularization—0
Meta-Learning with Network Pruning—0
Meta-Learning with Network Pruning for Overfitting Reduction—0
METAL: Minimum Effort Temporal Activity Localization in Untrimmed Videos—0
MetaMix: Improved Meta-Learning with Interpolation-based Consistency Regularization—0
Meta Navigator: Search for a Good Adaptation Policy for Few-shot Learning—0
MetaNorm: Learning to Normalize Few-Shot Batches Across Domains—0
Metaphor Detection for Low Resource Languages: From Zero-Shot to Few-Shot Learning in Middle High German—0
Meta R-CNN : Towards General Solver for Instance-level Few-shot Learning—0
Meta Reasoning over Knowledge Graphs—0
MetaRF: Differentiable Random Forest for Reaction Yield Prediction with a Few Trails—0
MetaSLRCL: A Self-Adaptive Learning Rate and Curriculum Learning Based Framework for Few-Shot Text Classification—0
Meta-Tasks: An alternative view on Meta-Learning Regularization—0
Meta-training with Demonstration Retrieval for Efficient Few-shot Learning—0
Meta-Transfer Derm-Diagnosis: Exploring Few-Shot Learning and Transfer Learning for Skin Disease Classification in Long-Tail Distribution—0
Meta-tuning Loss Functions and Data Augmentation for Few-shot Object Detection—0
Meta-World Conditional Neural Processes—0
Intelligent Repetition Counting for Unseen Exercises: A Few-Shot Learning Approach with Sensor Signals—0
Metric-Based Few-Shot Learning for Video Action Recognition—0
Feature Representation in Deep Metric Embeddings—0
Metric Learning with Background Noise Class for Few-shot Detection of Rare Sound Events—0
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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