Deep CNN for binary classification of images as human-made art, trained on large dataset of human-made artworks along with semi-curated scraped non-art images. The trained network was used to iteratively optimize an image toward maximizing activations, with the idea of visualizing the network’s concept of what makes an image qualify as art. Proposed and implemented intermediate random transform layers during training and image synthesis, which vastly increased the network’s perceptual range (over 50% improvement in large test set classification accuracy) without needing any additional data.
