  Deep learning architectures have achieved state-of-the-art (SOTA) performance
on computer vision tasks such as object detection and image segmentation. This
may be attributed to the use of over-parameterized, monolithic deep learning
architectures executed on large datasets. Although such architectures lead to
increased accuracy, this is usually accompanied by a large increase in
computation and memory requirements during inference. While this is a non-issue
in traditional machine learning pipelines, the recent confluence of machine
learning and fields like the Internet of Things has rendered such large
architectures infeasible for execution in low-resource settings. In such
settings, previous efforts have proposed decision cascades where inputs are
passed through models of increasing complexity until desired performance is
achieved. However, we argue that cascaded prediction leads to increased
computational cost due to wasteful intermediate computations. To address this,
we propose PaSeR (Parsimonious Segmentation with Reinforcement Learning) a
non-cascading, cost-aware learning pipeline as an alternative to cascaded
architectures. Through experimental evaluation on real-world and standard
datasets, we demonstrate that PaSeR achieves better accuracy while minimizing
computational cost relative to cascaded models. Further, we introduce a new
metric IoU/GigaFlop to evaluate the balance between cost and performance. On
the real-world task of battery material phase segmentation, PaSeR yields a
minimum performance improvement of 174% on the IoU/GigaFlop metric with respect
to baselines. We also demonstrate PaSeR's adaptability to complementary models
trained on a noisy MNIST dataset, where it achieved a minimum performance
improvement on IoU/GigaFlop of 13.4% over SOTA models. Code and data are
available at https://github.com/scailab/paser .
