Frequency domain classification shows promise with hybrid VGG/ViT models

A new research paper explores the effectiveness of image classification models in the frequency domain compared to the spatial domain. The study found that while both domains contain the same information, models trained in the frequency domain often underperform due to a lack of local features. By demonstrating that frequency domain accuracy is relatively stable even when local frequency data is swapped, the research proposes a hybrid model combining frequency and spatial domain training to achieve superior classification accuracy. This hybrid approach was tested using VGG and ViT architectures on various datasets, including ImageNet-100 and WikiArt. AI

IMPACT Proposes a hybrid model that could improve accuracy in image classification tasks by leveraging both frequency and spatial domain features.

RANK_REASON The cluster contains a research paper detailing a novel approach to image classification. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• arXiv
• Document Type Definition
• ImageNet-100
• KTH-TIPS2-b
• PSVRT
• Vgg Neural Network
• Vít
• WikiArt

• paper
• model release

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