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Typical deep convolutional architectures present an increasing number offeature maps as we go deeper in the network, whereas spatial resolution ofinputs is decreased through downsampling operations. This means that most ofthe parameters lay in the final layers, while a large portion of thecomputations are performed by a small fraction of the total parameters in thefirst 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 ofthe model. ThriftyNet achieves competitive performance on a tiny parametersbudget, exceeding 91% accuracy on CIFAR-10 with less than 40K parameters intotal, and 74.3% on CIFAR-100 with less than 600K parameters.

ThriftyNets : Convolutional Neural Networks with Tiny Parameter Budget