Siamese Labels Auxiliary Network(SiLaNet)
  Auxiliary information attracts more and more attention in the area of machine
learning. Attempts so far to include such auxiliary information in
state-of-the-art learning process have often been based on simply appending
these auxiliary features to the data level or feature level. In this paper, we
intend to propose a novel training method with new options and architectures.
Siamese labels, which were used in the training phase as auxiliary modules.
While in the testing phase, the auxiliary module should be removed. Siamese
label module makes it easier to train and improves the performance in testing
process. In general, the main contributions can be summarized as, 1) Siamese
Labels are firstly proposed as auxiliary information to improve the learning
efficiency; 2) We establish a new architecture, Siamese Labels Auxiliary
Network (SilaNet), which is to assist the training of the model; 3) Siamese
Labels Auxiliary Network is applied to compress the model parameters by 50% and
ensure the high accuracy at the same time. For the purpose of comparison, we
tested the network on CIFAR-10 and CIFAR100 using some common models. The
proposed SilaNet performs excellent efficiency both on the accuracy and
robustness.
