用于照片美学不平衡分类的基于 CNN 的重复自修正学习
CNN-based Repetitive self-revised learning for photos' aesthetics imbalanced classification
AI总结：
针对照片美学等级分布不平衡问题，提出基于 CNN 的重复自修正学习方法，通过迭代剔除中间等级低似然样本并利用双网络提取高光区域，提升美学分类性能。
摘要：
美学评估具有主观性，且美学等级的分布并不均衡。为实现照片美学的自动评估，我们专注于利用重复自修正学习（RSRL），通过不平衡数据集训练基于 CNN 的美学分类网络。在 RSRL 中，网络以重复方式进行训练：依据先前训练好的网络，从训练数据集中剔除处于中间美学等级、可能性较低的照片样本。此外，保留下来的两个网络被用于提取照片中与美学评估相关的高光区域。实验结果表明，基于 CNN 的重复自修正学习能够有效提升不平衡分类的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
