A Hybrid Deep Neural Network for Online Learning
Trupti R. Chavan, Abhijeet Vijay Nandedkar · 2017
The use of deep neural networks for artificial intelligence tasks is increasing day by day. However, incremental learning in such networks is a challenging task. This paper deals with learning new classes by using pre-trained model without scratch training. The famous VGGNET architecture is used for classification and can be viewed as cascaded structure of convolutional layers and a classifier. A hybrid VGGNET model containing offline and online trained network is introduced for incremental leaning. The offline trained network which plays an important role in feature extraction, is fixed with pre-trained conventional network. While the online trained network is adaptable and tuned to learn new classes. The key benefit of such learning is that without scratch training, a huge reduction in learning time and computations is achieved. The experimental results obtained on Caltech 101 dataset show that the performance of this hybrid model is comparable to end to end training.