  Even though convolutional neural networks can classify objects in images very
accurately, it is well known that the attention of the network may not always
be on the semantically important regions of the scene. It has been observed
that networks often learn background textures which are not relevant to the
object of interest. In turn this makes the networks susceptible to variations
and changes in the background which negatively affect their performance. We
propose a new two-step training procedure called split training to reduce this
bias in CNNs on both Infrared imagery and RGB data. Our split training
procedure has two steps: using MSE loss first train the layers of the network
on images with background to match the activations of the same network when it
is trained using images without background; then with these layers frozen,
train the rest of the network with cross-entropy loss to classify the objects.
Our training method outperforms the traditional training procedure in both a
simple CNN architecture, and deep CNNs like VGG and Densenet which use lots of
hardware resources, and learns to mimic human vision which focuses more on
shape and structure than background with higher accuracy.
