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Deep neural networks (DNNs) are so over-parametrized that recent research hasfound them to already contain a subnetwork with high accuracy at their randomlyinitialized state. Finding these subnetworks is a viable alternative trainingmethod to weight learning. In parallel, another line of work has hypothesizedthat deep residual networks (ResNets) are trying to approximate the behaviourof shallow recurrent neural networks (RNNs) and has proposed a way forcompressing them into recurrent models. This paper proposes blending theselines of research into a highly compressed yet accurate model: Hidden-FoldNetworks (HFNs). By first folding ResNet into a recurrent structure and thensearching for an accurate subnetwork hidden within the randomly initializedmodel, a high-performing yet tiny HFN is obtained without ever updating theweights. As a result, HFN achieves equivalent performance to ResNet50 onCIFAR100 while occupying 38.5x less memory, and similar performance to ResNet34on ImageNet with a memory size 26.8x smaller. The HFN will become even moreattractive by minimizing data transfers while staying accurate when it runs onhighly-quantized and randomly-weighted DNN inference accelerators. Codeavailable at https://github.com/Lopez-Angel/hidden-fold-networks

Hidden-Fold Networks: Random Recurrent Residuals Using Sparse Supermasks