 One way to do so, is to exploit the model variants known as "Regularized Autoencoders".Various techniques exist to prevent autoencoders from learning the identity function and to improve their ability to capture important information and learn richer representations.Recently, it has been observed that when representations are learnt in a way that encourages sparsity, improved performance is obtained on classification tasks.