Bridgeout: stochastic bridge regularization for deep neural networks

TL;DR

Bridgeout is a novel stochastic regularization method for deep neural networks that learns an optimal weight penalty norm, leading to sparser models and better classification performance than existing methods.

Contribution

It introduces Bridgeout, which generalizes existing stochastic regularization techniques by enabling learning of the penalty norm from data.

Findings

Bridgeout produces sparser model weights.

It improves gradients and classification accuracy.

Outperforms Dropout and Shakeout on various datasets.

Abstract

A major challenge in training deep neural networks is overfitting, i.e. inferior performance on unseen test examples compared to performance on training examples. To reduce overfitting, stochastic regularization methods have shown superior performance compared to deterministic weight penalties on a number of image recognition tasks. Stochastic methods such as Dropout and Shakeout, in expectation, are equivalent to imposing a ridge and elastic-net penalty on the model parameters, respectively. However, the choice of the norm of weight penalty is problem dependent and is not restricted to $\{L_1,L_2\}$. Therefore, in this paper we propose the Bridgeout stochastic regularization technique and prove that it is equivalent to an $L_q$ penalty on the weights, where the norm $q$ can be learned as a hyperparameter from data. Experimental results show that Bridgeout results in sparse model weights, improved gradients and superior classification performance compared to Dropout and Shakeout on synthetic and real datasets.