Efficient Training of Very Deep Neural Networks for Supervised Hashing

Understand

In this paper, we propose training very deep neural networks (DNNs) for supervised learning of hash codes.

• Existing methods in this context train relatively "shallow" networks limited by the issues arising in back propagation (e.e.
• vanishing gradients) as well as computational efficiency.
• We propose a novel and efficient training algorithm inspired by alternating direction method of multipliers (ADMM) that overcomes some of these limitations.