Regularising Knowledge Transfer by Meta Functional Learning

Machine learning classifiers’ capability is largely dependent on the scale of available training data and limited by the model overfitting in data-scarce learning tasks. To address this problem, this work proposes a novel Meta Functional Learning (MFL) by meta-learning a generalisable functional model from data-rich tasks whilst simultaneously regularising knowledge transfer to data-scarce tasks. The MFL computes meta-knowledge on functional regularisation generalisable to different learning tasks by which functional training on limited labelled data promotes more discriminative functions to be learned. Moreover, we adopt an Iterative Update strategy on MFL (MFL-IU). This improves knowledge transfer regularisation from MFL by progressively learning the functional regularisation in knowledge transfer. Experiments on three Few-Shot Learning (FSL) benchmarks (miniImageNet, CIFAR-FS and CUB) show that meta functional learning for regularisation knowledge transfer can benefit improving FSL classifiers.

Authors

• Pan Li Intelligent Game and Decision Lab
• Yanwei Fu Fudan University Shanghai Innovation Institute
• Shaogang Gong Queen Mary University of London

Keywords

• Machine Learning: Classification
• Machine Learning: Transfer, Adaptation, Multi-task Learning
• Machine Learning: Weakly Supervised Learning