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 151–200 of 2964 papers

TitleStatusHype
Is LLM an Overconfident Judge? Unveiling the Capabilities of LLMs in Detecting Offensive Language with Annotation DisagreementCode0
WatchGuardian: Enabling User-Defined Personalized Just-in-Time Intervention on Smartwatch—0
Transforming Multimodal Models into Action Models for Radiotherapy—0
OmniRL: In-Context Reinforcement Learning by Large-Scale Meta-Training in Randomized Worlds—0
RoboGrasp: A Universal Grasping Policy for Robust Robotic Control—0
An Analysis of LLM Fine-Tuning and Few-Shot Learning for Flaky Test Detection and Classification—0
FewTopNER: Integrating Few-Shot Learning with Topic Modeling and Named Entity Recognition in a Multilingual FrameworkCode0
Can LLMs Assist Annotators in Identifying Morality Frames? -- Case Study on Vaccination Debate on Social Media—0
Learning to Learn Weight Generation via Local Consistency Diffusion—0
Memory-Efficient Fine-Tuning of Transformers via Token SelectionCode0
Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs—0
ISAM-MTL: Cross-subject multi-task learning model with identifiable spikes and associative memory networks—0
Unraveling the Capabilities of Language Models in News SummarizationCode0
Distilling Large Language Models for Network Active Queue Management—0
One Head Eight Arms: Block Matrix based Low Rank Adaptation for CLIP-based Few-Shot Learning—0
AdaSemSeg: An Adaptive Few-shot Semantic Segmentation of Seismic Facies—0
Few Edges Are Enough: Few-Shot Network Attack Detection with Graph Neural Networks—0
Closed-Form Feedback-Free Learning with Forward ProjectionCode0
SampleLLM: Optimizing Tabular Data Synthesis in Recommendations—0
Evaluating Data Influence in Meta Learning—0
Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification—0
A Zero-Shot LLM Framework for Automatic Assignment Grading in Higher EducationCode0
Geometric Mean Improves Loss For Few-Shot Learning—0
Evaluating and Improving Graph to Text Generation with Large Language ModelsCode0
CVOCSemRPL: Class-Variance Optimized Clustering, Semantic Information Injection and Restricted Pseudo Labeling based Improved Semi-Supervised Few-Shot Learning—0
Comprehensive Modeling and Question Answering of Cancer Clinical Practice Guidelines using LLMs—0
Towards Safer Social Media Platforms: Scalable and Performant Few-Shot Harmful Content Moderation Using Large Language Models—0
Text-driven Online Action DetectionCode0
Adaptive Few-Shot Learning (AFSL): Tackling Data Scarcity with Stability, Robustness, and Versatility—0
Rethinking the Sample Relations for Few-Shot ClassificationCode7
Adapting OpenAI's CLIP Model for Few-Shot Image Inspection in Manufacturing Quality Control: An Expository Case Study with Multiple Application Examples—0
Patent Figure Classification using Large Vision-language ModelsCode0
MEDFORM: A Foundation Model for Contrastive Learning of CT Imaging and Clinical Numeric Data in Multi-Cancer AnalysisCode0
Zero-shot and Few-shot Learning with Instruction-following LLMs for Claim Matching in Automated Fact-checking—0
ACE: Anatomically Consistent Embeddings in Composition and DecompositionCode0
Efficient Few-Shot Medical Image Analysis via Hierarchical Contrastive Vision-Language Learning—0
I Can Find You in Seconds! Leveraging Large Language Models for Code Authorship Attribution—0
LeapVAD: A Leap in Autonomous Driving via Cognitive Perception and Dual-Process ThinkingCode2
An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering—0
A Comprehensive Evaluation of Large Language Models on Mental Illnesses in Arabic Context—0
Enhancing Unsupervised Graph Few-shot Learning via Set Functions and Optimal TransportCode0
ActPC-Geom: Towards Scalable Online Neural-Symbolic Learning via Accelerating Active Predictive Coding with Information Geometry & Diverse Cognitive Mechanisms—0
Hidden Entity Detection from GitHub Leveraging Large Language ModelsCode0
RW-Net: Enhancing Few-Shot Point Cloud Classification with a Wavelet Transform Projection-based Network—0
Holistic Semantic Representation for Navigational Trajectory GenerationCode1
Generalization-Enhanced Few-Shot Object Detection in Remote SensingCode1
Integrating Domain Knowledge into Large Language Models for Enhanced Fashion Recommendations—0
Online Meta-Learning Channel Autoencoder for Dynamic End-to-end Physical Layer Optimization—0
ValuesRAG: Enhancing Cultural Alignment Through Retrieval-Augmented Contextual Learning—0
State-of-the-art AI-based Learning Approaches for Deepfake Generation and Detection, Analyzing Opportunities, Threading through Pros, Cons, and Future Prospects—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