Fine-Grained Categorization Using a Mixture of Transfer Learning Networks

In this paper, we apply a mixture of experts approach to further enhance the accuracy of transfer learning networks on a fine-grained categorization problem, expanding on the work of Firsching and Hashem. Mixture of experts approaches may help to improve accuracy on categorization problems. Likewise, transfer learning is a highly effective technique for solving problems in machine learning of varying complexities. We here illustrate that mixtures of trained transfer learning networks, when applied properly, may further improve categorization accuracy.