Can a powerful neural network be a teacher for a weaker neural network?

Abstract

The transfer learning technique is widely used to learning in one context and applying it to another, i.e. the capacity to apply acquired knowledge and skills to new situations. But is it possible to transfer the learning from a deep neural network to a weaker neural network? Is it possible to improve the performance of a weak neural network using the knowledge acquired by a more powerful neural network? In this work, during the training process of a weak network, we add a loss function that minimizes the distance between the features previously learned from a strong neural network with the features that the weak network must try to learn. To demonstrate the effectiveness and robustness of our approach, we conducted a large number of experiments using three known datasets and demonstrated that a weak neural network can increase its performance if its learning process is driven by a more powerful neural network.

References in corpus (10)

• Adam: A Method for Stochastic Optimization
• Distilling the Knowledge in a Neural Network
• Practical Bayesian Optimization of Machine Learning Algorithms
• Neural Architecture Search: A Survey
• An Analysis of Deep Neural Network Models for Practical Applications
• Resnet in Resnet: Generalizing Residual Architectures
• Scalable Bayesian Optimization Using Deep Neural Networks
• A Comprehensive Survey on Transfer Learning
• FixyNN: Efficient Hardware for Mobile Computer Vision via Transfer Learning
• Connectivity Learning in Multi-Branch Networks