  Previous studies on image classification have mainly focused on the
performance of the networks, not on real-time operation or model compression.
We propose a Gaussian Deep Recurrent visual Attention Model (GDRAM)- a
reinforcement learning based lightweight deep neural network for large scale
image classification that outperforms the conventional CNN (Convolutional
Neural Network) which uses the entire image as input. Highly inspired by the
biological visual recognition process, our model mimics the stochastic location
of the retina with Gaussian distribution. We evaluate the model on Large
cluttered MNIST, Large CIFAR-10 and Large CIFAR-100 datasets which are resized
to 128 in both width and height.
