  Convolutional neural network (CNN) is widely used in computer vision
applications. In the networks that deal with images, CNNs are the most
time-consuming layer of the networks. Usually, the solution to address the
computation cost is to decrease the number of trainable parameters. This
solution usually comes with the cost of dropping the accuracy. Another problem
with this technique is that usually the cost of memory access is not taken into
account which results in insignificant speedup gain. The number of operations
and memory access in a standard convolution layer is independent of the input
content, which makes it limited for certain accelerations. We propose a simple
modification to a standard convolution to bridge this gap. We propose an
adaptive convolution that adopts different kernel sizes (or radii) based on the
content. The network can learn and select the proper radius based on the input
content in a soft decision manner. Our proposed radius-adaptive convolutional
neural network (RACNN) has a similar number of weights to a standard one, yet,
results show it can reach higher speeds. The code has been made available at:
https://github.com/meisamrf/racnn.
