A Principled Hierarchical Deep Learning Approach to Joint Image Compression and Classification

• URL: http://arxiv.org/abs/2310.19675v1
• Date: Mon, 30 Oct 2023 15:52:18 GMT
• Title: A Principled Hierarchical Deep Learning Approach to Joint Image Compression and Classification
• Authors: Siyu Qi, Achintha Wijesinghe, Lahiru D. Chamain, Zhi Ding
• Abstract summary: This work proposes a three-step joint learning strategy to guide encoders to extract features that are compact, discriminative, and amenable to common augmentations/transformations. Tests show that our proposed method achieves accuracy improvement of up to 1.5% on CIFAR-10 and 3% on CIFAR-100 over conventional E2E cross-entropy training.
• Score: 27.934109301041595
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Among applications of deep learning (DL) involving low cost sensors, remote image classification involves a physical channel that separates edge sensors and cloud classifiers. Traditional DL models must be divided between an encoder for the sensor and the decoder + classifier at the edge server. An important challenge is to effectively train such distributed models when the connecting channels have limited rate/capacity. Our goal is to optimize DL models such that the encoder latent requires low channel bandwidth while still delivers feature information for high classification accuracy. This work proposes a three-step joint learning strategy to guide encoders to extract features that are compact, discriminative, and amenable to common augmentations/transformations. We optimize latent dimension through an initial screening phase before end-to-end (E2E) training. To obtain an adjustable bit rate via a single pre-deployed encoder, we apply entropy-based quantization and/or manual truncation on the latent representations. Tests show that our proposed method achieves accuracy improvement of up to 1.5% on CIFAR-10 and 3% on CIFAR-100 over conventional E2E cross-entropy training.

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