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cs.LG2024
Meta-Task: A Method-Agnostic Framework for Learning to Regularize in Few-Shot Learning
Mohammad Rostami, Atik Faysal, Huaxia Wang +1
Overfitting is a significant challenge in Few-Shot Learning (FSL), where models trained on small, variable datasets tend to memorize rather than generalize to unseen tasks. Regular…
cs.LG2023
Unsupervised Representation Learning to Aid Semi-Supervised Meta Learning
Atik Faysal, Mohammad Rostami, Huaxia Wang +2
Few-shot learning or meta-learning leverages the data scarcity problem in machine learning. Traditionally, training data requires a multitude of samples and labeling for supervised…