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Top1. Introduction
With the quick advancement and deployment of information technologies, there are gigantic volumes of information in various organizations accessible on the Internet, for example, video, image, and medical data. The need of mining helpful data from these huge information poses a great challenge to the AI people group. Typical AI techniques requiring hand-crafted features cannot discover hidden information from data and may experience the ill effects of either data misfortune or overfitting. Conversely, DNNs have been effectively applied in a wide range of AI applications and delivered incredible outcomes in computational intelligence, e.g., video processing, image classification, speech recognition, and computer visual recognition.
Until recently, machine learning techniques can partition into generative and discriminative strategies. At present, the most commonly utilized DNN are generative models, e.g., the Deep Belief Networks (Hinton et al., 2006), the Restricted Boltzmann Machines (Salakhutdinov et al., 2007), and the Deep Boltzmann Machines (Salakhutdinov et al., 2009). These techniques prepared the log-probability gradient prepared to utilize MCMC-based strategies that turn out to be progressively uncertain as preparing advances. It is because examples from the Markov Chains cannot blend between models sufficiently quickly. Moreover, generative models, e.g. the Autoencoder (AE) (Bengio et al., 2009), the Variational Autoencoder (Kingma and Welling, 2014; Mescheder et al., 2017; Tan et al., 2018), and the Important Weighted Autoencoders (Burda et al., 2016) have created to utilize direct back-proliferation for preparing and maintaining a strategic distance from challenges yielded by the MCMC preparation. Each of these strategies considered as the projection that yields a considerable classification result by anticipating tests from the original feature space into a projected space with a better class-separability for pattern classification problems (Wasikowski et al., 2010). Among them, the AE (Bengio et al., 2009) is an unsupervised feature learning technique that aims to recover the representation to be roughly equivalent to the original sources of the inputs. The number of hidden units is normally bigger than the number of feature dimensions for feature representation learning. The projection at the hidden layer of the AE yields a helpful representation of the original sources of inputs (Bengio et al., 2009).