Deep Convolutional Neural Networks (DCNN) require millions of labeled
training examples for image classification and object detection tasks, which
restrict these models to domains where such datasets are available. In this
paper, we explore the use of unsupervised sparse coding applied to stereo-video
data to help alleviate the need for large amounts of labeled data. We show that
replacing a typical supervised convolutional layer with an unsupervised
sparse-coding layer within a DCNN allows for better performance on a car
detection task when only a limited number of labeled training examples is
available. Furthermore, the network that incorporates sparse coding allows for
more consistent performance over varying initializations and ordering of
training examples when compared to a fully supervised DCNN. Finally, we compare
activations between the unsupervised sparse-coding layer and the supervised
convolutional layer, and show that the sparse representation exhibits an
encoding that is depth selective, whereas encodings from the convolutional
layer do not exhibit such selectivity. These result indicates promise for using
unsupervised sparse-coding approaches in real-world computer vision tasks in
domains with limited labeled training data
