# Changing the Image Memorability: From Basic Photo Editing to GANs

arXiv: https://arxiv.org/abs/1811.03825 · HTML: https://mlsanity.com/paper/1811.03825/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2018.

## Built on by (most cited first, 7 of 7)

- [A Circular-Structured Representation for Visual Emotion Distribution Learning](https://mlsanity.com/paper/2106.12450/) (2021)
- [Memorability: An image-computable measure of information utility](https://mlsanity.com/paper/2104.00805/) (2021)
- [Seeking Subjectivity in Visual Emotion Distribution Learning](https://mlsanity.com/paper/2207.11875/) (2022)
- [Controlling Memorability of Face Images](https://mlsanity.com/paper/2202.11896/) (2022)
- [Comprehensive Literature Survey on Deep Learning used in Image Memorability Prediction and Modification](https://mlsanity.com/paper/2301.06080/) (2022)
- [Seeing Eye to AI: Comparing Human Gaze and Model Attention in Video Memorability](https://mlsanity.com/paper/2311.16484/) (2023)
- [How to Take a Memorable Picture? Empowering Users with Actionable Feedback](https://mlsanity.com/paper/2602.21877/) (2026)

## Abstract (arXiv)

Memorability is considered to be an important characteristic of visual content, whereas for advertisement and educational purposes it is often crucial. Despite numerous studies on understanding and predicting image memorability, there are almost no achievements in memorability modification. In this work, we study two approaches to image editing - GAN and classical image processing - and show their impact on memorability. The visual features which influence memorability directly stay unknown till now, hence it is impossible to control it manually. As a solution, we let GAN learn it deeply using labeled data, and then use it for conditional generation of new images. By analogy with algorithms which edit facial attributes, we consider memorability as yet another attribute and operate with it in the same way. Obtained data is also interesting for analysis, simply because there are no real-world examples of successful change of image memorability while preserving its other attributes. We believe this may give many new answers to the question "what makes an image memorable?" Apart from that we also study the influence of conventional photo-editing tools (Photoshop, Instagram, etc.) used daily by a wide audience on memorability. In this case, we start from real practical methods and study it using statistics and recent advances in memorability prediction. Photographers, designers, and advertisers will benefit from the results of this study directly.