# CNN-based Repetitive self-revised learning for photos' aesthetics imbalanced classification

arXiv: https://arxiv.org/abs/2003.03081 · HTML: https://mlsanity.com/paper/2003.03081/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2020.

## Built on by (most cited first, 2 of 2)

- [Exploring to establish an appropriate model for image aesthetic assessment via CNN-based RSRL: An empirical study](https://mlsanity.com/paper/2106.03316/) (2021)
- [Exploring CNN-based models for image's aesthetic score prediction with using ensemble](https://mlsanity.com/paper/2210.05119/) (2022)

## Abstract (arXiv)

Aesthetic assessment is subjective, and the distribution of the aesthetic levels is imbalanced. In order to realize the auto-assessment of photo aesthetics, we focus on using repetitive self-revised learning (RSRL) to train the CNN-based aesthetics classification network by imbalanced data set. As RSRL, the network is trained repetitively by dropping out the low likelihood photo samples at the middle levels of aesthetics from the training data set based on the previously trained network. Further, the retained two networks are used in extracting highlight regions of the photos related with the aesthetic assessment. Experimental results show that the CNN-based repetitive self-revised learning is effective for improving the performances of the imbalanced classification.