Neural Routing in Meta Learning
- URL: http://arxiv.org/abs/2210.07932v1
- Date: Fri, 14 Oct 2022 16:31:24 GMT
- Title: Neural Routing in Meta Learning
- Authors: Jicang Cai, Saeed Vahidian, Weijia Wang, Mohsen Joneidi, and Bill Lin
- Abstract summary: We aim to improve the model performance of the current meta learning algorithms by selectively using only parts of the model conditioned on the input tasks.
In this work, we describe an approach that investigates task-dependent dynamic neuron selection in deep convolutional neural networks (CNNs) by leveraging the scaling factor in the batch normalization layer.
We find that the proposed approach, neural routing in meta learning (NRML), outperforms one of the well-known existing meta learning baselines on few-shot classification tasks.
- Score: 9.070747377130472
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Meta-learning often referred to as learning-to-learn is a promising notion
raised to mimic human learning by exploiting the knowledge of prior tasks but
being able to adapt quickly to novel tasks. A plethora of models has emerged in
this context and improved the learning efficiency, robustness, etc. The
question that arises here is can we emulate other aspects of human learning and
incorporate them into the existing meta learning algorithms? Inspired by the
widely recognized finding in neuroscience that distinct parts of the brain are
highly specialized for different types of tasks, we aim to improve the model
performance of the current meta learning algorithms by selectively using only
parts of the model conditioned on the input tasks. In this work, we describe an
approach that investigates task-dependent dynamic neuron selection in deep
convolutional neural networks (CNNs) by leveraging the scaling factor in the
batch normalization (BN) layer associated with each convolutional layer. The
problem is intriguing because the idea of helping different parts of the model
to learn from different types of tasks may help us train better filters in
CNNs, and improve the model generalization performance. We find that the
proposed approach, neural routing in meta learning (NRML), outperforms one of
the well-known existing meta learning baselines on few-shot classification
tasks on the most widely used benchmark datasets.
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