  One of the most promising ways of improving the performance of deep
convolutional neural networks is by increasing the number of convolutional
layers. However, adding layers makes training more difficult and
computationally expensive. In order to train deeper networks, we propose to add
auxiliary supervision branches after certain intermediate layers during
training. We formulate a simple rule of thumb to determine where these branches
should be added. The resulting deeply supervised structure makes the training
much easier and also produces better classification results on ImageNet and the
recently released, larger MIT Places dataset
