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 12011250 of 2964 papers

TitleStatusHype
EVA-X: A Foundation Model for General Chest X-ray Analysis with Self-supervised LearningCode0
DrugLLM: Open Large Language Model for Few-shot Molecule Generation0
Intra-task Mutual Attention based Vision Transformer for Few-Shot Learning0
Multi-Agent RL-Based Industrial AIGC Service Offloading over Wireless Edge Networks0
Enhancing News Summarization with ELearnFit through Efficient In-Context Learning and Efficient Fine-Tuning0
Open-SQL Framework: Enhancing Text-to-SQL on Open-source Large Language Models0
A Survey of Few-Shot Learning for Biomedical Time Series0
UniGen: Universal Domain Generalization for Sentiment Classification via Zero-shot Dataset GenerationCode0
A separability-based approach to quantifying generalization: which layer is best?Code0
Accelerating Convergence in Bayesian Few-Shot ClassificationCode0
Exploring Self-Supervised Vision Transformers for Deepfake Detection: A Comparative AnalysisCode0
Variational Neuron Shifting for Few-Shot Image Classification Across Domains0
StablePT: Towards Stable Prompting for Few-shot Learning via Input SeparationCode0
PEFSL: A deployment Pipeline for Embedded Few-Shot Learning on a FPGA SoC0
PEVA-Net: Prompt-Enhanced View Aggregation Network for Zero/Few-Shot Multi-View 3D Shape Recognition0
Ta-Adapter: Enhancing few-shot CLIP with task-aware encoders0
Certification of Speaker Recognition Models to Additive PerturbationsCode0
Evaluation of Few-Shot Learning for Classification Tasks in the Polish Language0
Meta In-Context Learning Makes Large Language Models Better Zero and Few-Shot Relation Extractors0
Empowering Large Language Models for Textual Data Augmentation0
Meta-Transfer Derm-Diagnosis: Exploring Few-Shot Learning and Transfer Learning for Skin Disease Classification in Long-Tail Distribution0
Beyond Deepfake Images: Detecting AI-Generated Videos0
A comprehensive and easy-to-use multi-domain multi-task medical imaging meta-dataset (MedIMeta)0
Graph Machine Learning in the Era of Large Language Models (LLMs)0
Identifying Fairness Issues in Automatically Generated Testing Content0
Text-dependent Speaker Verification (TdSV) Challenge 2024: Challenge Evaluation Plan0
Stance Detection on Social Media with Fine-Tuned Large Language Models0
Many-Shot In-Context Learning0
CryoMAE: Few-Shot Cryo-EM Particle Picking with Masked Autoencoders0
Improving Recall of Large Language Models: A Model Collaboration Approach for Relational Triple Extraction0
Conditional Prototype Rectification Prompt LearningCode0
GeMQuAD : Generating Multilingual Question Answering Datasets from Large Language Models using Few Shot Learning0
PM2: A New Prompting Multi-modal Model Paradigm for Few-shot Medical Image Classification0
Navigating the Landscape of Large Language Models: A Comprehensive Review and Analysis of Paradigms and Fine-Tuning StrategiesCode0
ChatGPT and general-purpose AI count fruits in pictures surprisingly well0
Sketch-Plan-Generalize: Learning and Planning with Neuro-Symbolic Programmatic Representations for Inductive Spatial Concepts0
Using Few-Shot Learning to Classify Primary Lung Cancer and Other Malignancy with Lung Metastasis in Cytological Imaging via Endobronchial Ultrasound Procedures0
Few-Shot Object Detection: Research Advances and Challenges0
Robust Few-Shot Ensemble Learning with Focal Diversity-Based PruningCode0
Negatives Make A Positive: An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning0
SSwsrNet: A Semi-Supervised Few-Shot Learning Framework for Wireless Signal Recognition0
Non-negative Subspace Feature Representation for Few-shot Learning in Medical Imaging0
Class-Incremental Few-Shot Event Detection0
Is Meta-training Really Necessary for Molecular Few-Shot Learning ?0
Small Language Models Learn Enhanced Reasoning Skills from Medical Textbooks0
MFORT-QA: Multi-hop Few-shot Open Rich Table Question Answering0
Cross-System Categorization of Abnormal Traces in Microservice-Based Systems via Meta-Learning0
Real Acoustic Fields: An Audio-Visual Room Acoustics Dataset and Benchmark0
PLOT-TAL -- Prompt Learning with Optimal Transport for Few-Shot Temporal Action Localization0
Boosting Few-Shot Learning with Disentangled Self-Supervised Learning and Meta-Learning for Medical Image Classification0
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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