Visual aesthetic analysis using deep neural network: model and techniques to increase accuracy without transfer learning

12/09/2017

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by   Muktabh Mayank Srivastava, et al.

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We train a deep Convolutional Neural Network (CNN) from scratch for visual aesthetic analysis in images and discuss techniques we adopt to improve the accuracy. We avoid the prevalent best transfer learning approaches of using pretrained weights to perform the task and train a model from scratch to get accuracy of 78.7 We further show that accuracy increases to 81.48 set by incremental 10 percentile of entire AVA dataset showing our algorithm gets better with more data.