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- W4385825288 abstract "Among major deep learning (DL) applications, distributed learning involving image classification require effective image compression codecs deployed on low-cost sensing devices for efficient transmission and storage. Traditional codecs such as JPEG designed for perceptual quality are not configured for DL tasks. This work introduces an integrative end-to-end trainable model for image compression and classification consisting of a JPEG image codec and a DL-based classifier. We demonstrate how this model can optimize the widely deployed JPEG codec settings to improve classification accuracy in consideration of bandwidth constraint. Our tests on CIFAR-100 and ImageNet also demonstrate improved validation accuracy over preset JPEG configuration." @default.
- W4385825288 created "2023-08-15" @default.
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- W4385825288 date "2023-08-10" @default.
- W4385825288 modified "2023-10-16" @default.
- W4385825288 title "End-to-End Optimization of JPEG-Based Deep Learning Process for Image Classification" @default.
- W4385825288 doi "https://doi.org/10.48550/arxiv.2308.05840" @default.
- W4385825288 hasPublicationYear "2023" @default.
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