  Dropout is a well-known regularization method by sampling a sub-network from
a larger deep neural network and training different sub-networks on different
subsets of the data. Inspired by the dropout concept, we propose EDropout as an
energy-based framework for pruning neural networks in classification tasks. In
this approach, a set of binary pruning state vectors (population) represents a
set of corresponding sub-networks from an arbitrary provided original neural
network. An energy loss function assigns a scalar energy loss value to each
pruning state. The energy-based model stochastically evolves the population to
find states with lower energy loss. The best pruning state is then selected and
applied to the original network. Similar to dropout, the kept weights are
updated using backpropagation in a probabilistic model. The energy-based model
again searches for better pruning states and the cycle continuous. Indeed, this
procedure is in fact switching between the energy model, which manages the
pruning states, and the probabilistic model, which updates the temporarily
unpruned weights, in each iteration. The population can dynamically converge to
a pruning state. This can be interpreted as dropout leading to pruning the
network. From an implementation perspective, EDropout can prune typical neural
networks without modification of the network architecture. We evaluated the
proposed method on different flavours of ResNets, AlexNet, and SqueezeNet on
the Kuzushiji, Fashion, CIFAR-10, CIFAR-100, and Flowers datasets, and compared
the pruning rate and classification performance of the models. On average the
networks trained with EDropout achieved a pruning rate of more than $50\%$ of
the trainable parameters with approximately $<5\%$ and $<1\%$ drop of Top-1 and
Top-5 classification accuracy, respectively.
