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 2201–2250 of 2964 papers

TitleStatusHype
Exploring Sentiment Dynamics and Predictive Behaviors in Cryptocurrency Discussions by Few-Shot Learning with Large Language Models—0
Exploring structure diversity in atomic resolution microscopy with graph neural networks—0
Exploring the Capabilities of LLMs for IMU-based Fine-grained Human Activity Understanding—0
Exploring the Generalization of Cancer Clinical Trial Eligibility Classifiers Across Diseases—0
Exploring the LLM Journey from Cognition to Expression with Linear Representations—0
Exploring the MIT Mathematics and EECS Curriculum Using Large Language Models—0
Exploring the Space of Key-Value-Query Models with Intention—0
External-Memory Networks for Low-Shot Learning of Targets in Forward-Looking-Sonar Imagery—0
EyeDAS: Securing Perception of Autonomous Cars Against the Stereoblindness Syndrome—0
Face Behavior a la carte: Expressions, Affect and Action Units in a Single Network—0
FACE: Few-shot Adapter with Cross-view Fusion for Cross-subject EEG Emotion Recognition—0
Facial Landmark Correlation Analysis—0
FAD: Frequency Adaptation and Diversion for Cross-domain Few-shot Learning—0
Fair Few-shot Learning with Auxiliary Sets—0
Fast Adaptation with Kernel and Gradient based Meta Leaning—0
Generalized Adaptation for Few-Shot Learning—0
Fast Task Adaptation for Few-Shot Learning—0
Fast visual grounding in interaction: bringing few-shot learning with neural networks to an interactive robot—0
Feature Activation Map: Visual Explanation of Deep Learning Models for Image Classification—0
Feature Aligning Few shot Learning Method Using Local Descriptors Weighted Rules—0
Feature Transformation Ensemble Model with Batch Spectral Regularization for Cross-Domain Few-Shot Classification—0
Federated Few-Shot Learning with Adversarial Learning—0
Federated Large Language Models: Feasibility, Robustness, Security and Future Directions—0
Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification—0
FewFedPIT: Towards Privacy-preserving and Few-shot Federated Instruction Tuning—0
Few Edges Are Enough: Few-Shot Network Attack Detection with Graph Neural Networks—0
FewFedWeight: Few-shot Federated Learning Framework across Multiple NLP Tasks—0
Fewmatch: Dynamic Prototype Refinement for Semi-Supervised Few-Shot Learning—0
Few-Round Learning for Federated Learning—0
FewSense, Towards a Scalable and Cross-Domain Wi-Fi Sensing System Using Few-Shot Learning—0
Few-shot 3D LiDAR Semantic Segmentation for Autonomous Driving—0
Few-Shot 3D Point Cloud Semantic Segmentation via Stratified Class-Specific Attention Based Transformer Network—0
Few-Shot Abstract Visual Reasoning With Spectral Features—0
Few-shot acoustic event detection via meta-learning—0
Few-Shot Action Recognition with Compromised Metric via Optimal Transport—0
Few-shot Action Recognition with Implicit Temporal Alignment and Pair Similarity Optimization—0
Few Shot Activity Recognition Using Variational Inference—0
Few-Shot Adaptation for Multimedia Semantic Indexing—0
Few-Shot Adaptation of Grounding DINO for Agricultural Domain—0
Few-Shot Adversarial Domain Adaptation—0
Few-Shot Airway-Tree Modeling using Data-Driven Sparse Priors—0
Few-shot Anomaly Detection in Text with Deviation Learning—0
Few-Shot Authorship Attribution in English Reddit Posts—0
Few-Shot Batch Incremental Road Object Detection via Detector Fusion—0
Few-Shot Bayesian Optimization with Deep Kernel Surrogates—0
Few-Shot Bearing Fault Diagnosis Based on Model-Agnostic Meta-Learning—0
Few-Shot Causal Representation Learning for Out-of-Distribution Generalization on Heterogeneous Graphs—0
Few-Shot Classification in Unseen Domains by Episodic Meta-Learning Across Visual Domains—0
Few-shot Classification on Graphs with Structural Regularized GCNs—0
Domain-Agnostic Few-Shot Classification by Learning Disparate Modulators—0
Show:102550
← PrevPage 45 of 60Next →

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