Transferred Fusion Learning using Skipped Networks
This work addresses the need for enhanced visual intelligence in intelligent systems, but appears incremental as it builds on existing transfer and zero-shot learning methods.
The paper tackles the problem of improving object recognition by proposing a transferred fusion learning mechanism that uses a student architecture where networks learn from each other, resulting in a model that outperforms existing models.
Identification of an entity that is of interest is prominent in any intelligent system. The visual intelligence of the model is enhanced when the capability of recognition is added. Several methods such as transfer learning and zero shot learning help to reuse the existing models or augment the existing model to achieve improved performance at the task of object recognition. Transferred fusion learning is one such mechanism that intends to use the best of both worlds and build a model that is capable of outperforming the models involved in the system. We propose a novel mechanism to amplify the process of transfer learning by introducing a student architecture where the networks learn from each other.