  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.
