ArticlePloS one2021
Compare the performance of the models in art classification.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- A Class-Wise Deep Ensemble Framework Using ResNet101 and DenseNet201 for Brain Tumor Classification.Journal of imaging informatics in medicine · 2026Article
- Artistic style classification in traditional cultural heritage using convolutional neural networks.Scientific reports · 2026Article
- An art style classification network integrating contrastive learning and counterfactual attention.PloS one · 2026Article
- Social cognitive impacts of digital art in metaverse environments: A cross-sectional study of user perception and behavioral patterns.PloS one · 2026Article
- Using ensemble learning for classifying artistic styles in traditional Chinese woodcuts.Scientific reports · 2025Article
- A novel model for cultural-based classification of liberal arts using deep reinforcement learning.Scientific reports · 2025Article
- How deep is your art: An experimental study on the limits of artistic understanding in a single-task, single-modality neural network.PloS one · 2024Article
- Prediction of emotion distribution of images based on weightedFrontiers in computational neuroscience · 2024Article
- Backward induction-based deep image search.PloS one · 2024Article
- Big Transfer Learning for Fine Art Classification.Computational intelligence and neuroscience · 2022Article
- Fine-Tuning Convolutional Neural Networks for COVID-19 Detection from Chest X-ray Images.Diagnostics (Basel, Switzerland) · 2021Article
- Deep learning for COVID-19 detection based on CT images.Scientific reports · 2021Article
- Comparing CAM Algorithms for the Identification of Salient Image Features in Iconography Artwork Analysis.Journal of imaging · 2021Article
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Authors and funding
4 authors.
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Abstract
Because large numbers of artworks are preserved in museums and galleries, much work must be done to classify these works into genres, styles and artists. Recent technological advancements have enabled an increasing number of artworks to be digitized. Thus, it is necessary to teach computers to analyze (e.g., classify and annotate) art to assist people in performing such tasks. In this study, we tested 7 different models on 3 different datasets under the same experimental setup to compare their art classification performances when either using or not using transfer learning. The models were compared based on their abilities for classifying genres, styles and artists. Comparing the result with previous work shows that the model performance can be effectively improved by optimizing the model structure, and our results achieve state-of-the-art performance in all classification tasks with three datasets. In addition, we visualized the process of style and genre classification to help us understand the difficulties that computers have when tasked with classifying art. Finally, we used the trained models described above to perform similarity searches and obtained performance improvements.
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