Energy-based Dropout in Restricted Boltzmann Machines: Why not go random
This addresses overfitting in energy-based models like RBMs, offering a more informed regularization method, though it appears incremental as it builds on existing Dropout concepts.
The paper tackles overfitting in deep learning by proposing an energy-based Dropout (E-Dropout) that uses model energy to decide which neurons to drop, rather than random selection, and shows improved performance over traditional Dropout and standard Restricted Boltzmann Machines on benchmark datasets.
Deep learning architectures have been widely fostered throughout the last years, being used in a wide range of applications, such as object recognition, image reconstruction, and signal processing. Nevertheless, such models suffer from a common problem known as overfitting, which limits the network from predicting unseen data effectively. Regularization approaches arise in an attempt to address such a shortcoming. Among them, one can refer to the well-known Dropout, which tackles the problem by randomly shutting down a set of neurons and their connections according to a certain probability. Therefore, this approach does not consider any additional knowledge to decide which units should be disconnected. In this paper, we propose an energy-based Dropout (E-Dropout) that makes conscious decisions whether a neuron should be dropped or not. Specifically, we design this regularization method by correlating neurons and the model's energy as an importance level for further applying it to energy-based models, such as Restricted Boltzmann Machines (RBMs). The experimental results over several benchmark datasets revealed the proposed approach's suitability compared to the traditional Dropout and the standard RBMs.