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
Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Prompts0
Amortized Bayesian Meta-Learning0
How to fine-tune deep neural networks in few-shot learning?0
Human-Free Automated Prompting for Vision-Language Anomaly Detection: Prompt Optimization with Meta-guiding Prompt Scheme0
Dehazing Remote Sensing and UAV Imagery: A Review of Deep Learning, Prior-based, and Hybrid Approaches0
Adaptive Weighted Co-Learning for Cross-Domain Few-Shot Learning0
How Fine-Tuning Allows for Effective Meta-Learning0
Deficiency of Large Language Models in Finance: An Empirical Examination of Hallucination0
Benchmarking Large Language Model Capabilities for Conditional Generation0
How Can LLMs and Knowledge Graphs Contribute to Robot Safety? A Few-Shot Learning Approach0
LAraBench: Benchmarking Arabic AI with Large Language Models0
Deep Transfer Learning with Graph Neural Network for Sensor-Based Human Activity Recognition0
A Mixture-of-Experts Approach to Few-Shot Task Transfer in Open-Ended Text Worlds0
How does Architecture Influence the Base Capabilities of Pre-trained Language Models? A Case Study Based on FFN-Wider and MoE Transformers0
How Important is Domain Specificity in Language Models and Instruction Finetuning for Biomedical Relation Extraction?0
Deep metric learning improves lab of origin prediction of genetically engineered plasmids0
Deep Meta-Learning: Learning to Learn in the Concept Space0
BEAN: Interpretable Representation Learning with Biologically-Enhanced Artificial Neuronal Assembly Regularization0
A MIMO Radar-Based Metric Learning Approach for Activity Recognition0
Deep Learning of Unified Region, Edge, and Contour Models for Automated Image Segmentation0
Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey0
Deep learning based detection of collateral circulation in coronary angiographies0
A MIMO Radar-based Few-Shot Learning Approach for Human-ID0
Adaptive Transfer Learning: a simple but effective transfer learning0
A Comprehensive Overview and Survey of Recent Advances in Meta-Learning0
How Reliable AI Chatbots are for Disease Prediction from Patient Complaints?0
TEGEE: Task dEfinition Guided Expert Ensembling for Generalizable and Few-shot Learning0
Bayesian Embeddings for Few-Shot Open World Recognition0
Deep few-shot learning for bi-temporal building change detection0
Batch Group Normalization0
A metric learning approach for endoscopic kidney stone identification0
Deep Double-Side Learning Ensemble Model for Few-Shot Parkinson Speech Recognition0
DeepCRCEval: Revisiting the Evaluation of Code Review Comment Generation0
Bad Form: Comparing Context-Based and Form-Based Few-Shot Learning in Distributional Semantic Models0
AltGDmin: Alternating GD and Minimization for Partly-Decoupled (Federated) Optimization0
HMAE: Self-Supervised Few-Shot Learning for Quantum Spin Systems0
HMSN: Hyperbolic Self-Supervised Learning by Clustering with Ideal Prototypes0
DeepCode AI Fix: Fixing Security Vulnerabilities with Large Language Models0
Decomposed Prototype Learning for Few-Shot Scene Graph Generation0
Baby steps towards few-shot learning with multiple semantics0
ALWNN Empowered Automatic Modulation Classification: Conquering Complexity and Scarce Sample Conditions0
3D-VirtFusion: Synthetic 3D Data Augmentation through Generative Diffusion Models and Controllable Editing0
GNN-SKAN: Harnessing the Power of SwallowKAN to Advance Molecular Representation Learning with GNNs0
DCP: Learning Accelerator Dataflow for Neural Network via Propagation0
Dataset Bias Prediction for Few-Shot Image Classification0
A Weak Supervision Approach for Few-Shot Aspect Based Sentiment0
High-level semantic feature matters few-shot unsupervised domain adaptation0
How Secure Are Large Language Models (LLMs) for Navigation in Urban Environments?0
Human in the loop: How to effectively create coherent topics by manually labeling only a few documents per class0
Dataset Bias in Few-shot Image Recognition0
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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