Current approaches for Knowledge Distillation (KD) either directly use training data or sample from the training data distribution. In this paper, we demonstrate effectiveness of 'mismatched' unlabeled stimulus to perform KD for image classification networks. For illustration, we consider scenarios where this is a complete absence of training data, or mismatched stimulus has to be used for augmenting a small amount of training data. We demonstrate that stimulus complexity is a key factor for distillation's good performance. Our examples include use of various datasets for stimulating MNIST and CIFAR teachers.
Knowledge Distillation (KD) is the process of transferring the generalization ability of a teacher model (usually large neural network) to a student model (usually a small neural network). @cite_3 demonstrated that the student network can be trained using an input data with combination of hard labels as well as soft labels. The hard labels are the ground truth labels (one-hot vectors) available for the training data while the soft labels are the output of the teacher network on the input data. Most of the distillation approaches either directly use training data or device a strategy to learn the training data distribution and then sample from it. However, the assumption of availability of labeled training data may not always hold true, due to various reasons.
@cite_3
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A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions. Unfortunately, making predictions using a whole ensemble of models is cumbersome and may be too computationally expensive to allow deployment to a large number of users, especially if the individual models are large neural nets. Caruana and his collaborators have shown that it is possible to compress the knowledge in an ensemble into a single model which is much easier to deploy and we develop this approach further using a different compression technique. We achieve some surprising results on MNIST and we show that we can significantly improve the acoustic model of a heavily used commercial system by distilling the knowledge in an ensemble of models into a single model. We also introduce a new type of ensemble composed of one or more full models and many specialist models which learn to distinguish fine-grained classes that the full models confuse. Unlike a mixture of experts, these specialist models can be trained rapidly and in parallel.