Learning-based image/video codecs typically utilizethe well known auto-encoder structure where the encoder trans-forms input data to a low-dimensional latent representation.Efficient latent encoding can reduce bandwidth needs duringcompression for transmission and storage. In this paper, weexamine the effect of assigning high level coarse grouping labelsto each latent vector. Designing coding profiles for each latentgroup can achieve high compression encoding. We show thatsuch grouping can be learned via end-to-end optimization of thecodec and the deep learning (DL) model to optimize rate-accuracyfor a given data set. For cloud-based inference, source encodercan select a coding profile based on its learned grouping andencode the data features accordingly. Our test results on imageclassification show that significant performance improvementcan be achieved with learned grouping over its non-groupingcounterpart.
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End-to-End Image Classification and Compression with variational autoencoders
The past decade has witnessed the rising dominance
of deep learning and artificial intelligence in a wide range of
applications. In particular, the ocean of wireless smartphones and
IoT devices continue to fuel the tremendous growth of edge/cloudbased
machine learning (ML) systems including image/speech
recognition and classification. To overcome the infrastructural
barrier of limited network bandwidth in cloud ML, existing
solutions have mainly relied on traditional compression codecs
such as JPEG that were historically engineered for humanend
users instead of ML algorithms. Traditional codecs do not
necessarily preserve features important to ML algorithms under
limited bandwidth, leading to potentially inferior performance.
This work investigates application-driven optimization of programmable
commercial codec settings for networked learning
tasks such as image classification. Based on the foundation
of variational autoencoders (VAEs), we develop an end-to-end
networked learning framework by jointly optimizing the codec
and classifier without reconstructing images for given data rate
(bandwidth). Compared with standard JPEG codec, the proposed
VAE joint compression and classification framework achieves
classification accuracy improvement by over 10% and 4%,
respectively, for CIFAR-10 and ImageNet-1k data sets at data rate
of 0.8 bpp. Our proposed VAE-based models show 65%99% reductions
in encoder size, 1.5 13.1 improvements in inference
speed and 25%99% savings in power compared to baseline
models. We further show that a simple decoder can reconstruct
images with sufficient quality without compromising classification
accuracy.
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- NSF-PAR ID:
- 10347113
- Date Published:
- Journal Name:
- IEEE Internet of Things Journal
- ISSN:
- 2372-2541
- Page Range / eLocation ID:
- 1 to 1
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/JIOT.2022.3182313
