Neighbourhood Distillation: On the benefits of non end-to-end distillation

End-to-end training with back propagation is the standard method for training deep neural networks. However, as networks become deeper and bigger, endto-end training becomes more challenging: highly non-convex models gets stuck easily in local optima, gradients signals are prone to vanish or explode during back-propagation, training requires computational resources and time. In this work, we propose to break away from the end-to-end paradigm in the context of Knowledge Distillation. Instead of distilling a model end-to-end, we propose to split it into smaller sub-networks - also called neighbourhoods - that are then trained independently. We empirically show that distilling networks in a non endto-end fashion can be beneficial in a diverse range of use cases. First, we show that it speeds up Knowledge Distillation by exploiting parallelism and training on smaller networks. Second, we show that independently distilled neighbourhoods may be efficiently reused for Neural Architecture Search. Finally, because smaller networks model simpler functions, we show that they are easier to train with synthetic data than their deeper counterparts. As Deep Neural Networks improve on challenging tasks, they also become deeper and bigger. Convolutional neural networks for Image Classification grew from 5 layers in LeNet (LeCun et al., 1998) to more than a 100 in the latest ResNet models (He et al., 2016). However, as models grow in size, training by back propagating gradients through the entire network becomes more challenging and computationally expensive.