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 1351–1400 of 2964 papers

TitleStatusHype
Few-Shot Causal Representation Learning for Out-of-Distribution Generalization on Heterogeneous Graphs—0
Aspect-Based Few-Shot Learning—0
AgEval: A Benchmark for Zero-Shot and Few-Shot Plant Stress Phenotyping with Multimodal LLMs—0
Few-Shot Bearing Fault Diagnosis Based on Model-Agnostic Meta-Learning—0
Few-Shot Bayesian Optimization with Deep Kernel Surrogates—0
Few-Shot Batch Incremental Road Object Detection via Detector Fusion—0
Collaboration of Pre-trained Models Makes Better Few-shot Learner—0
Few-Shot Authorship Attribution in English Reddit Posts—0
Few-shot Anomaly Detection in Text with Deviation Learning—0
CohortGPT: An Enhanced GPT for Participant Recruitment in Clinical Study—0
Few-Shot Airway-Tree Modeling using Data-Driven Sparse Priors—0
Supervised Graph Contrastive Learning for Few-shot Node Classification—0
A general-purpose AI assistant embedded in an open-source radiology information system—0
Few-Shot Adversarial Domain Adaptation—0
Few-Shot Adaptation of Grounding DINO for Agricultural Domain—0
Few-Shot Adaptation for Multimedia Semantic Indexing—0
Code Generation Tools (Almost) for Free? A Study of Few-Shot, Pre-Trained Language Models on Code—0
Few Shot Activity Recognition Using Variational Inference—0
Few-shot Action Recognition with Implicit Temporal Alignment and Pair Similarity Optimization—0
CodeCoT: Tackling Code Syntax Errors in CoT Reasoning for Code Generation—0
Few-Shot Action Recognition with Compromised Metric via Optimal Transport—0
CoCoP: Enhancing Text Classification with LLM through Code Completion Prompt—0
A Simple Task-aware Contrastive Local Descriptor Selection Strategy for Few-shot Learning between inter class and intra class—0
A Game-Theoretic Perspective of Generalization in Reinforcement Learning—0
Adapting OpenAI's CLIP Model for Few-Shot Image Inspection in Manufacturing Quality Control: An Expository Case Study with Multiple Application Examples—0
Adapting Language-Audio Models as Few-Shot Audio Learners—0
Few-shot acoustic event detection via meta-learning—0
Few-Shot Abstract Visual Reasoning With Spectral Features—0
Few-Shot 3D Point Cloud Semantic Segmentation via Stratified Class-Specific Attention Based Transformer Network—0
COBRA: COmBinatorial Retrieval Augmentation for Few-Shot Adaptation—0
Few-shot 3D LiDAR Semantic Segmentation for Autonomous Driving—0
FewSense, Towards a Scalable and Cross-Domain Wi-Fi Sensing System Using Few-Shot Learning—0
Few-Round Learning for Federated Learning—0
Towards Cross-Granularity Few-Shot Learning: Coarse-to-Fine Pseudo-Labeling with Visual-Semantic Meta-Embedding—0
A Similarity Paradigm Through Textual Regularization Without Forgetting—0
Fewmatch: Dynamic Prototype Refinement for Semi-Supervised Few-Shot Learning—0
CO3: Low-resource Contrastive Co-training for Generative Conversational Query Rewrite—0
FewFedWeight: Few-shot Federated Learning Framework across Multiple NLP Tasks—0
Few Edges Are Enough: Few-Shot Network Attack Detection with Graph Neural Networks—0
A Self-Adaptive Learning Rate and Curriculum Learning Based Framework for Few-Shot Text Classification—0
A Framework of Meta Functional Learning for Regularising Knowledge Transfer—0
FewFedPIT: Towards Privacy-preserving and Few-shot Federated Instruction Tuning—0
Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification—0
Federated Large Language Models: Feasibility, Robustness, Security and Future Directions—0
Federated Few-Shot Learning with Adversarial Learning—0
Clustering Algorithms and RAG Enhancing Semi-Supervised Text Classification with Large LLMs—0
Feature Transformation Ensemble Model with Batch Spectral Regularization for Cross-Domain Few-Shot Classification—0
CLR-GAM: Contrastive Point Cloud Learning with Guided Augmentation and Feature Mapping—0
Feature Aligning Few shot Learning Method Using Local Descriptors Weighted Rules—0
Feature Activation Map: Visual Explanation of Deep Learning Models for Image Classification—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