DEVELOPING EFFICIENT DEEP ARCHITECTURES FOR CLASSIFICATION TASK

The success of deep architectures in tackling difficult issues has contributed to their rising appeal. Particularly, deep architectures have shown to be useful in a wide range of classification problems. Whereas prior work needed expertly built and constructed feature representations, deep architectures aim to automatically build representation hierarchies from data. In this work, we investigate the feasibility and promise of the proposed architecture on a variety of benchmark datasets, showing the suggested architecture's capacity to provide discriminative feature representations that are able to achieve excellent performance. In addition, we examine a variety of test datasets to see how the proposed III design stacks up against other, similarly tuned, existing architectures. Compared to the other strategies we tested, the suggested architecture's discriminating and generalization performance are much higher, as shown by our extensive empirical investigation.