  Typical deep convolutional architectures present an increasing number of
feature maps as we go deeper in the network, whereas spatial resolution of
inputs is decreased through downsampling operations. This means that most of
the parameters lay in the final layers, while a large portion of the
computations are performed by a small fraction of the total parameters in the
first layers. In an effort to use every parameter of a network at its maximum,
we propose a new convolutional neural network architecture, called ThriftyNet.
In ThriftyNet, only one convolutional layer is defined and used recursively,
leading to a maximal parameter factorization. In complement, normalization,
non-linearities, downsamplings and shortcut ensure sufficient expressivity of
the model. ThriftyNet achieves competitive performance on a tiny parameters
budget, exceeding 91% accuracy on CIFAR-10 with less than 40K parameters in
total, and 74.3% on CIFAR-100 with less than 600K parameters.
