In this paper we describe the problem of painter classification, and propose
a novel approach based on deep convolutional autoencoder neural networks. While
previous approaches relied on image processing and manual feature extraction
from paintings, our approach operates on the raw pixel level, without any
preprocessing or manual feature extraction. We first train a deep convolutional
autoencoder on a dataset of paintings, and subsequently use it to initialize a
supervised convolutional neural network for the classification phase.
The proposed approach substantially outperforms previous methods, improving
the previous state-of-the-art for the 3-painter classification problem from
90.44% accuracy (previous state-of-the-art) to 96.52% accuracy, i.e., a 63%
reduction in error rate.

本文提出一种基于深度卷积自编码神经网络的新方法，用于画家分类问题，相对于先前的图像处理和手动特征提取，该方法在原始像素级别进行操作，采用深度卷积自编码和有监督的卷积神经网络，将原有的 90.44％分类准确率提高到 96.52％。