# Brain-Inspired Deep Networks for Image Aesthetics Assessment

arXiv: https://arxiv.org/abs/1601.04155 · HTML: https://mlsanity.com/paper/1601.04155/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2016.

## Built on by (most cited first, 6 of 6)

- [User-Guided Personalized Image Aesthetic Assessment based on Deep Reinforcement Learning](https://mlsanity.com/paper/2106.07488/) (2021)
- [A Survey on Image Aesthetic Assessment](https://mlsanity.com/paper/2103.11616/) (2021)
- [UMAIR-FPS: User-aware Multi-modal Animation Illustration Recommendation Fusion with Painting Style](https://mlsanity.com/paper/2402.10381/) (2024)
- [Finding Beautiful and Happy Images for Mental Health and Well-being Applications](https://mlsanity.com/paper/2404.18109/) (2024)
- [Clutter Detection and Removal by Multi-Objective Analysis for Photographic Guidance](https://mlsanity.com/paper/2507.14553/) (2025)
- [From Concepts to Judgments: Interpretable Image Aesthetic Assessment](https://mlsanity.com/paper/2603.18108/) (2026)

## Abstract (arXiv)

Image aesthetics assessment has been challenging due to its subjective nature. Inspired by the scientific advances in the human visual perception and neuroaesthetics, we design Brain-Inspired Deep Networks (BDN) for this task. BDN first learns attributes through the parallel supervised pathways, on a variety of selected feature dimensions. A high-level synthesis network is trained to associate and transform those attributes into the overall aesthetics rating. We then extend BDN to predicting the distribution of human ratings, since aesthetics ratings are often subjective. Another highlight is our first-of-its-kind study of label-preserving transformations in the context of aesthetics assessment, which leads to an effective data augmentation approach. Experimental results on the AVA dataset show that our biological inspired and task-specific BDN model gains significantly performance improvement, compared to other state-of-the-art models with the same or higher parameter capacity.