Single-Image Super-Resolution can support robotic tasks in environments where
a reliable visual stream is required to monitor the mission, handle
teleoperation or study relevant visual details. In this work, we propose an
efficient Generative Adversarial Network model for real-time Super-Resolution.
We adopt a tailored architecture of the original SRGAN and model quantization
to boost the execution on CPU and Edge TPU devices, achieving up to 200 fps
inference. We further optimize our model by distilling its knowledge to a
smaller version of the network and obtain remarkable improvements compared to
the standard training approach. Our experiments show that our fast and
lightweight model preserves considerably satisfying image quality compared to
heavier state-of-the-art models. Finally, we conduct experiments on image
transmission with bandwidth degradation to highlight the advantages of the
proposed system for mobile robotic applications.

本文提出了一个高效的生成对抗网络模型用于在需要可靠的视觉流来监视任务、处理远程操作或研究相关视觉细节的环境中支持机器人任务的单图像超分辨率，并通过定制的网络架构和模型量化使其在 CPU 和 Edge TPU 设备上达到高达 200 fps 的快速推断，同时进一步通过将其知识压缩至网络的较小版本来优化我们的模型，并与标准训练方法进行比较，得出显著的性能改善结果。我们的实验结果表明，相比于更重型的现有模型，我们的快速轻量模型在保持极高图像质量的同时，具有显著的优势，在带宽降级的图像传输方面的应用潜力也值得进一步挖掘。

使用知识蒸馏的对抗生成超分辨率在边缘设备上的应用

Generative Adversarial Super-Resolution at the Edge with Knowledge Distillation

While neural network hardware accelerators provide a substantial amount of
raw compute throughput, the models deployed on them must be co-designed for the
underlying hardware architecture to obtain the optimal system performance. We
present a class of computer vision models designed using hardware-aware neural
architecture search and customized to run on the Edge TPU, Google's neural
network hardware accelerator for low-power, edge devices. For the Edge TPU in
Coral devices, these models enable real-time image classification performance
while achieving accuracy typically seen only with larger, compute-heavy models
running in data centers. On Pixel 4's Edge TPU, these models improve the
accuracy-latency tradeoff over existing SoTA mobile models.

采用硬件意识神经架构搜索，设计了一类适用于 Edge TPU 的计算机视觉模型，有效提升了实时图像分类性能并在像素 4 的边缘 TPU 上改善了精度 - 延迟权衡。