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 1301–1350 of 2964 papers

TitleStatusHype
Few-shot Image Recognition with Manifolds—0
Comprehensive Modeling and Question Answering of Cancer Clinical Practice Guidelines using LLMs—0
A Minimalist Dataset for Systematic Generalization of Perception, Syntax, and Semantics—0
Few-Shot Image Generation by Conditional Relaxing Diffusion Inversion—0
Compositional Prototype Network with Multi-view Comparision for Few-Shot Point Cloud Semantic Segmentation—0
Few-shot Image Classification with Multi-Facet Prototypes—0
Few-Shot Image Classification via Contrastive Self-Supervised Learning—0
Compositional Generalization via Neural-Symbolic Stack Machines—0
A Strong Baseline for Semi-Supervised Incremental Few-Shot Learning—0
Few-Shot Image Classification Along Sparse Graphs—0
Few-Shot Human Motion Prediction via Meta-Learning—0
Few-Shot Histopathology Image Classification: Evaluating State-of-the-Art Methods and Unveiling Performance Insights—0
Compositional Fine-Grained Low-Shot Learning—0
A Stochastic Approach to Bi-Level Optimization for Hyperparameter Optimization and Meta Learning—0
Malicious Requests Detection with Improved Bidirectional Long Short-term Memory Neural Networks—0
Few-shot graph link prediction with domain adaptation—0
Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification—0
Few-shot fine-tuning SOTA summarization models for medical dialogues—0
Few-Shot Few-Shot Learning and the role of Spatial Attention—0
Compositional Few-Shot Recognition with Primitive Discovery and Enhancing—0
Few-shot fault diagnosis based on multi-scale graph convolution filtering for industry—0
Composing Diffusion Policies for Few-shot Learning of Movement Trajectories—0
Assume, Augment and Learn: Unsupervised Few-Shot Meta-Learning via Random Labels and Data Augmentation—0
AgileNet: Lightweight Dictionary-based Few-shot Learning—0
Deep Representation Learning with an Information-theoretic Loss—0
Cross-System Categorization of Abnormal Traces in Microservice-Based Systems via Meta-Learning—0
Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification—0
Associative Adversarial Learning Based on Selective Attack—0
Few-Shot Cross-Lingual TTS Using Transferable Phoneme Embedding—0
Few-shot Continual Learning: a Brain-inspired Approach—0
Few-shot Continual Infomax Learning—0
Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark—0
Assessing two novel distance-based loss functions for few-shot image classification—0
Agile gesture recognition for low-power applications: customisation for generalisation—0
Adaptive Clipping for Privacy-Preserving Few-Shot Learning: Enhancing Generalization with Limited Data—0
Few-shot clinical entity recognition in English, French and Spanish: masked language models outperform generative model prompting—0
Few-Shot Class-Incremental Learning with Non-IID Decentralized Data—0
Compare learning: bi-attention network for few-shot learning—0
Few-shot Class-incremental Learning for Classification and Object Detection: A Survey—0
Comparative Analysis of Resource-Efficient CNN Architectures for Brain Tumor Classification—0
Assessing the Performance of the DINOv2 Self-supervised Learning Vision Transformer Model for the Segmentation of the Left Atrium from MRI Images—0
SENet: A Spectral Filtering Approach to Represent Exemplars for Few-shot Learning—0
Few-shot Classification with Hypersphere Modeling of Prototypes—0
Few-Shot Classification with Contrastive Learning—0
Communication-Efficient and Privacy-Preserving Decentralized Meta-Learning—0
Assertion Enhanced Few-Shot Learning: Instructive Technique for Large Language Models to Generate Educational Explanations—0
Domain-Agnostic Few-Shot Classification by Learning Disparate Modulators—0
Few-shot Classification on Graphs with Structural Regularized GCNs—0
Few-Shot Classification in Unseen Domains by Episodic Meta-Learning Across Visual Domains—0
Combat Data Shift in Few-shot Learning with Knowledge Graph—0
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All datasetsMedConceptsQADTDFGVC-AircraftMini-ImageNet - 5-Shot LearningMini-Imagenet 5-way (1-shot)Stanford CarsMini-ImageNet - 1-Shot LearningPubMedQACaltech101CaseHOLDEuroSATFlowers-102

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