Title: Deep Learning: Generalization Requires Deep Compositional Feature Space Design,
Abstract: Generalization error defines the discriminability and the representation
power of a deep model. In this work, we claim that feature space design using
deep compositional function plays a significant role in generalization along
with explicit and implicit regularizations. Our claims are being established
with several image classification experiments. We show that the information
loss due to convolution and max pooling can be marginalized with the
compositional design, improving generalization performance. Also, we will show
that learning rate decay acts as an implicit regularizer in deep model
training.