Visible to the public Masked Neural Style Transfer using Convolutional Neural Networks

TitleMasked Neural Style Transfer using Convolutional Neural Networks
Publication TypeConference Paper
Year of Publication2018
AuthorsHanda, A., Garg, P., Khare, V.
Conference Name2018 International Conference on Recent Innovations in Electrical, Electronics Communication Engineering (ICRIEECE)
KeywordsBiological neural networks, content image, convolutional neural nets, convolutional neural networks, deep neural networks, face recognition, feature extraction, Histograms, masked neural style transfer, masking, neural style transfer, object detection, Predictive Metrics, pubcrawl, Resiliency, Scalability, Semantics, Technological innovation, VGG-19 model, visual experiences, visual perception, visual perceptions, visualization

In painting, humans can draw an interrelation between the style and the content of a given image in order to enhance visual experiences. Deep neural networks like convolutional neural networks are being used to draw a satisfying conclusion of this problem of neural style transfer due to their exceptional results in the key areas of visual perceptions such as object detection and face recognition.In this study, along with style transfer on whole image it is also outlined how transfer of style can be performed only on the specific parts of the content image which is accomplished by using masks. The style is transferred in a way that there is a least amount of loss to the content image i.e., semantics of the image is preserved.

Citation Keyhanda_masked_2018