• Mohammed Yaser Ali
• Dr. G.R.Prasad
• SIMRAN CHUGH
• SANDEEP SURESH KUMAR YADAV
• KARTIK HAKIM

The promise of deep learning is to discover rich, hierarchical models [1] that represent probability distributions over the kinds of data encountered in ARTIFICIAL INTELLIGENCE applications, such as natural images. Deep Generative models had been less impact due to difficulty of approximating many intractable probabilistic computations that arises in maximum likelihood estimation and related strategies and due to difficulties of leveraging the benefits of piece wise linear units in generative context. This Paper elaborates the uses of GANS to overcome these difficulties by filtering images of the real world faces under the Generative models.

Artificial Intelligence, Deep Learning, Neural Networks

"IMAGE FILTERING AND PROCESSING USING ADVERSARIAL NETWORKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.4, Issue 3, page no.88-91, March-2017, Available :http://www.jetir.org/papers/JETIR1703019.pdf

"IMAGE FILTERING AND PROCESSING USING ADVERSARIAL NETWORKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.4, Issue 3, page no. pp88-91, March-2017, Available at : http://www.jetir.org/papers/JETIR1703019.pdf