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 5175 of 2964 papers

TitleStatusHype
Generative Pre-trained Autoregressive Diffusion Transformer0
TrumorGPT: Graph-Based Retrieval-Augmented Large Language Model for Fact-Checking0
Task-Adapter++: Task-specific Adaptation with Order-aware Alignment for Few-shot Action RecognitionCode0
Enhanced Urdu Intent Detection with Large Language Models and Prototype-Informed Predictive Pipelines0
User Behavior Analysis in Privacy Protection with Large Language Models: A Study on Privacy Preferences with Limited Data0
CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis0
Understanding In-context Learning of Addition via Activation Subspaces0
A Large Language Model for Feasible and Diverse Population Synthesis0
HMAE: Self-Supervised Few-Shot Learning for Quantum Spin Systems0
MISE: Meta-knowledge Inheritance for Social Media-Based Stressor EstimationCode0
Topology-Aware CLIP Few-Shot Learning0
SpectrumFM: A Foundation Model for Intelligent Spectrum ManagementCode1
Can Foundation Models Really Segment Tumors? A Benchmarking Odyssey in Lung CT Imaging0
Exploring internal representation of self-supervised networks: few-shot learning abilities and comparison with human semantics and recognition of objects0
Advance Fake Video Detection via Vision Transformers0
ProFi-Net: Prototype-based Feature Attention with Curriculum Augmentation for WiFi-based Gesture Recognition0
Enhancing TCR-Peptide Interaction Prediction with Pretrained Language Models and Molecular Representations0
Detecting Actionable Requests and Offers on Social Media During Crises Using LLMs0
AltGDmin: Alternating GD and Minimization for Partly-Decoupled (Federated) Optimization0
HFBRI-MAE: Handcrafted Feature Based Rotation-Invariant Masked Autoencoder for 3D Point Cloud Analysis0
A Baseline for Self-state Identification and Classification in Mental Health Data: CLPsych 2025 Task0
KAN or MLP? Point Cloud Shows the Way ForwardCode1
Chain-of-Thought Textual Reasoning for Few-shot Temporal Action Localization0
Knowledge Acquisition on Mass-shooting Events via LLMs for AI-Driven Justice0
Can GPT tell us why these images are synthesized? Empowering Multimodal Large Language Models for Forensics0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1gpt-4-0125-previewAccuracy61.91Unverified
2gpt-4-0125-previewAccuracy52.49Unverified
3gpt-3.5-turboAccuracy41.48Unverified
4gpt-3.5-turboAccuracy37.06Unverified
5johnsnowlabs/JSL-MedMNX-7BAccuracy25.63Unverified
6yikuan8/Clinical-LongformerAccuracy25.55Unverified
7BioMistral/BioMistral-7B-DAREAccuracy25.06Unverified
8yikuan8/Clinical-LongformerAccuracy25.04Unverified
9PharMolix/BioMedGPT-LM-7BAccuracy24.92Unverified
10PharMolix/BioMedGPT-LM-7BAccuracy24.75Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean67.27Unverified
2SaSPA + CAL4-shot Accuracy48.3Unverified
3Real-Guidance + CAL4-shot Accuracy41.5Unverified
4CAL4-shot Accuracy40.9Unverified
#ModelMetricClaimedVerifiedStatus
1SaSPA + CALHarmonic mean52.2Unverified
2CALHarmonic mean35.2Unverified
3Variational Prompt TuningHarmonic mean34.69Unverified
4Real-Guidance + CALHarmonic mean34.5Unverified
#ModelMetricClaimedVerifiedStatus
1BGNNAccuracy92.7Unverified
2TIM-GDAccuracy87.4Unverified
3UNEM-GaussianAccuracy66.4Unverified
#ModelMetricClaimedVerifiedStatus
1EASY (transductive)Accuracy82.75Unverified
2HCTransformers5 way 1~2 shot74.74Unverified
3HyperShotAccuracy53.18Unverified
#ModelMetricClaimedVerifiedStatus
1SaSPA + CAL4-shot Accuracy66.7Unverified
2Real-Guidance + CAL4-shot Accuracy44.3Unverified
3CAL4-shot Accuracy42.2Unverified
#ModelMetricClaimedVerifiedStatus
1HCTransformersAcc74.74Unverified
2DPGNAcc67.6Unverified
#ModelMetricClaimedVerifiedStatus
1MetaGen Blended RAG (zero-shot)Accuracy77.9Unverified
2CoT-T5-11B (1024 Shot)Accuracy73.42Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean96.44Unverified
#ModelMetricClaimedVerifiedStatus
1CoT-T5-11B (1024 Shot)Accuracy68.3Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean77.71Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean81.12Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean91.57Unverified
#ModelMetricClaimedVerifiedStatus
1CovidExpertAUC-ROC1Unverified
#ModelMetricClaimedVerifiedStatus
1CoT-T5-11B (1024 Shot)Accuracy78.02Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy65.7Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy73.2Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean96.82Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean73.07Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean78.51Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy52.3Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean79Unverified