Low-Quality Image Detection by Hierarchical VAE

• URL: http://arxiv.org/abs/2408.10885v1
• Date: Tue, 20 Aug 2024 14:14:15 GMT
• Title: Low-Quality Image Detection by Hierarchical VAE
• Abstract summary: This study addresses a new task of unsupervised detection of low-quality images. We propose a method that not only detects low-quality images with various types of degradation but also provides visual clues of them.
• Score: 5.2980803808373516
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To make an employee roster, photo album, or training dataset of generative models, one needs to collect high-quality images while dismissing low-quality ones. This study addresses a new task of unsupervised detection of low-quality images. We propose a method that not only detects low-quality images with various types of degradation but also provides visual clues of them based on an observation that partial reconstruction by hierarchical variational autoencoders fails for low-quality images. The experiments show that our method outperforms several unsupervised out-of-distribution detection methods and also gives visual clues for low-quality images that help humans recognize them even in thumbnail view.

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