A Principled Hierarchical Deep Learning Approach to Joint Image
  Compression and Classification
Siyu Qi Achintha Wijesinghe Lahiru D. Chamain Zhi Ding
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.