One-Shot Learning
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Benchmarks
MNIST
Most implemented
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Prototypical Networks for Few-shot Learning
Matching Networks for One Shot Learning
One-shot Learning with Memory-Augmented Neural Networks
Siamese neural networks for one-shot image recognition
One-Shot Learning for Semantic Segmentation
Papers
One Shot Learning for Edge Detection on Point Clouds
Each scanner possesses its unique characteristics and exhibits its distinct sampling error distribution. Training a network on a dataset that includes data collected from different scanners is less effective than trainin…
One-Shot LearningEdge DetectionPoint CloudsOne-shot learning for the complex dynamical behaviors of weakly nonlinear forced oscillators
Extrapolative prediction of complex nonlinear dynamics remains a central challenge in engineering. This study proposes a one-shot learning method to identify global frequency-response curves from a single excitation time…
One-Shot LearningTraining Data Size Sensitivity in Unsupervised Rhyme Recognition
Rhyme is deceptively intuitive: what is or is not a rhyme is constructed historically, scholars struggle with rhyme classification, and people disagree on whether two words are rhymed or not. This complicates automated r…
One-Shot LearningFederatedFactory: Generative One-Shot Learning for Extremely Non-IID Distributed Scenarios
Federated Learning (FL) enables distributed optimization without compromising data sovereignty. Yet, where local label distributions are mutually exclusive, standard weight aggregation fails due to conflicting optimizati…
Distributed OptimizationFederated LearningOne-Shot LearningRegime-aware financial volatility forecasting via in-context learning
This work introduces a regime-aware in-context learning framework that leverages large language models (LLMs) for financial volatility forecasting under nonstationary market conditions. The proposed approach deploys pret…
One-Shot LearningFrom Native Memes to Global Moderation: Cross-Cultural Evaluation of Vision-Language Models for Hateful Meme Detection
Cultural context profoundly shapes how people interpret online content, yet vision-language models (VLMs) remain predominantly trained through Western or English-centric lenses. This limits their fairness and cross-cultu…
One-Shot Learning