  We introduce Network Augmentation (NetAug), a new training method for
improving the performance of tiny neural networks. Existing regularization
techniques (e.g., data augmentation, dropout) have shown much success on large
neural networks by adding noise to overcome over-fitting. However, we found
these techniques hurt the performance of tiny neural networks. We argue that
training tiny models are different from large models: rather than augmenting
the data, we should augment the model, since tiny models tend to suffer from
under-fitting rather than over-fitting due to limited capacity. To alleviate
this issue, NetAug augments the network (reverse dropout) instead of inserting
noise into the dataset or the network. It puts the tiny model into larger
models and encourages it to work as a sub-model of larger models to get extra
supervision, in addition to functioning as an independent model. At test time,
only the tiny model is used for inference, incurring zero inference overhead.
We demonstrate the effectiveness of NetAug on image classification and object
detection. NetAug consistently improves the performance of tiny models,
achieving up to 2.2% accuracy improvement on ImageNet. On object detection,
achieving the same level of performance, NetAug requires 41% fewer MACs on
Pascal VOC and 38% fewer MACs on COCO than the baseline.
