Fine-tuning Convolutional Neural Networks for fine art classification
作者：
Highlights：
• We achieve state-of-the-art results for five fine art-related classification tasks.
• Different convolutional neural network fine-tuning strategies are explored.
• Impact of various source domain-dependent weight initialization is studied.
• Networks pre-trained for scene and sentiment recognition perform best for art tasks.
• Fine-tuned models can be used to retrieve images similar in style or content.
摘要
•We achieve state-of-the-art results for five fine art-related classification tasks.•Different convolutional neural network fine-tuning strategies are explored.•Impact of various source domain-dependent weight initialization is studied.•Networks pre-trained for scene and sentiment recognition perform best for art tasks.•Fine-tuned models can be used to retrieve images similar in style or content.
论文关键词：Painting classification,Deep learning,Convolutional Neural Networks,Fine-tuning strategies
论文评审过程：Received 20 February 2018, Revised 9 July 2018, Accepted 10 July 2018, Available online 12 July 2018, Version of Record 27 July 2018.
论文官网地址：https://doi.org/10.1016/j.eswa.2018.07.026
