# Enhancing Perceptual Attributes with Bayesian Style Generation

## TL;DR

This paper presents a versatile deep learning framework that modifies images to enhance perceptual attributes like memorability and scariness, outperforming existing methods on benchmark datasets.

## Contribution

It introduces a general deep learning approach combining style transfer and GANs to modify high-level perceptual attributes in images, applicable to multiple properties.

## Key findings

- Effective in increasing image memorability
- Capable of generating scarier images
- Outperforms state-of-the-art methods on benchmarks

## Abstract

Deep learning has brought an unprecedented progress in computer vision and significant advances have been made in predicting subjective properties inherent to visual data (e.g., memorability, aesthetic quality, evoked emotions, etc.). Recently, some research works have even proposed deep learning approaches to modify images such as to appropriately alter these properties. Following this research line, this paper introduces a novel deep learning framework for synthesizing images in order to enhance a predefined perceptual attribute. Our approach takes as input a natural image and exploits recent models for deep style transfer and generative adversarial networks to change its style in order to modify a specific high-level attribute. Differently from previous works focusing on enhancing a specific property of a visual content, we propose a general framework and demonstrate its effectiveness in two use cases, i.e. increasing image memorability and generating scary pictures. We evaluate the proposed approach on publicly available benchmarks, demonstrating its advantages over state of the art methods.