Making a Low-dimensional Representation Suitable for Diverse Tasks

Abstract

We introduce a new approach to the training of classifiers for performance on multiple tasks. The proposed hybrid training method leads to improved generalization via a better low-dimensional representation of the problem space. The quality of the representation is assessed by embedding it in a two-dimensional space using multi-dimensional scaling, allowing a direct visualization of the results. The performance of the approach is demonstrated on a highly non-linear image classification task.

Keywords:

• Imposing Bias On Neural Networks
• Multiple-task Training
• Transfer In Neural Networks