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 101150 of 2964 papers

TitleStatusHype
Reason-RFT: Reinforcement Fine-Tuning for Visual ReasoningCode3
Show or Tell? Effectively prompting Vision-Language Models for semantic segmentation0
FACE: Few-shot Adapter with Cross-view Fusion for Cross-subject EEG Emotion Recognition0
PNN: A Novel Progressive Neural Network for Fault Classification in Rotating Machinery under Small Dataset Constraint0
CFReID: Continual Few-shot Person Re-IdentificationCode0
ALWNN Empowered Automatic Modulation Classification: Conquering Complexity and Scarce Sample Conditions0
FS-SS: Few-Shot Learning for Fast and Accurate Spike Sorting of High-channel Count Probes0
ConvoGen: Enhancing Conversational AI with Synthetic Data: A Multi-Agent Approach0
Think or Not Think: A Study of Explicit Thinking in Rule-Based Visual Reinforcement Fine-TuningCode2
Corrective In-Context Learning: Evaluating Self-Correction in Large Language ModelsCode0
Sparseformer: a Transferable Transformer with Multi-granularity Token Sparsification for Medical Time Series Classification0
Conjuring Positive Pairs for Efficient Unification of Representation Learning and Image Synthesis0
Optimizing Large Language Models for Detecting Symptoms of Comorbid Depression or Anxiety in Chronic Diseases: Insights from Patient Messages0
Riemannian Geometric-based Meta Learning0
DRESS: Disentangled Representation-based Self-Supervised Meta-Learning for Diverse TasksCode0
Membership Inference Attacks fueled by Few-Short Learning to detect privacy leakage tackling data integrity0
Evaluation of the Automated Labeling Method for Taxonomic Nomenclature Through Prompt-Optimized Large Language Model0
From Dataset to Real-world: General 3D Object Detection via Generalized Cross-domain Few-shot Learning0
Memory Is All You Need: Testing How Model Memory Affects LLM Performance in Annotation Tasks0
Rethinking Few-Shot Medical Image Segmentation by SAM2: A Training-Free Framework with Augmentative Prompting and Dynamic Matching0
Bridging Molecular Graphs and Large Language ModelsCode1
Frankenstein Optimizer: Harnessing the Potential by Revisiting Optimization TricksCode0
ExpertGenQA: Open-ended QA generation in Specialized Domains0
Malware Classification from Memory Dumps Using Machine Learning, Transformers, and Large Language Models0
Use Me Wisely: AI-Driven Assessment for LLM Prompting Skills Development0
Network Traffic Classification Using Machine Learning, Transformer, and Large Language Models0
Diversity Covariance-Aware Prompt Learning for Vision-Language Models0
Enhancing Multi-hop Reasoning in Vision-Language Models via Self-Distillation with Multi-Prompt Ensembling0
Transformer Based Self-Context Aware Prediction for Few-Shot Anomaly Detection in Videos0
Learning to Animate Images from A Few Videos to Portray Delicate Human Actions0
LADs: Leveraging LLMs for AI-Driven DevOps0
Mixmamba-fewshot: mamba and attention mixer-based method with few-shot learning for bearing fault diagnosisCode1
Large Language Models as Attribution Regularizers for Efficient Model TrainingCode0
Few-Shot Multilingual Open-Domain QA from 5 ExamplesCode0
Code Summarization Beyond Function LevelCode1
Cross-domain Few-shot Object Detection with Multi-modal Textual EnrichmentCode1
An Autonomous Network Orchestration Framework Integrating Large Language Models with Continual Reinforcement Learning0
TimePFN: Effective Multivariate Time Series Forecasting with Synthetic DataCode1
A Similarity Paradigm Through Textual Regularization Without Forgetting0
Retrieving Versus Understanding Extractive Evidence in Few-Shot Learning0
Dual-level Mixup for Graph Few-shot Learning with Fewer TasksCode0
RM-PoT: Reformulating Mathematical Problems and Solving via Program of Thoughts0
Do we still need Human Annotators? Prompting Large Language Models for Aspect Sentiment Quad PredictionCode0
UniMatch: Universal Matching from Atom to Task for Few-Shot Drug DiscoveryCode0
RIDE: Enhancing Large Language Model Alignment through Restyled In-Context Learning Demonstration ExemplarsCode0
SQL-o1: A Self-Reward Heuristic Dynamic Search Method for Text-to-SQLCode2
SEM-CLIP: Precise Few-Shot Learning for Nanoscale Defect Detection in Scanning Electron Microscope Image0
A Hybrid Model for Few-Shot Text Classification Using Transfer and Meta-Learning0
Cancer Vaccine Adjuvant Name Recognition from Biomedical Literature using Large Language ModelsCode0
A Flag Decomposition for Hierarchical DatasetsCode0
Show:102550
← PrevPage 3 of 60Next →

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