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Found 4 papers, 1 papers with code
A Tale of Two Models: Constructing Evasive Attacks on Edge Models
In this paper, we introduce a new evasive attack, DIVA, that exploits these differences in edge adaptation, by adding adversarial noise to input data that maximizes the output difference between the original and adapted model.
Sampling Training Data for Continual Learning Between Robots and the Cloud
Today's robotic fleets are increasingly measuring high-volume video and LIDAR sensory streams, which can be mined for valuable training data, such as rare scenes of road construction sites, to steadily improve robotic perception models.
Characterizing and Taming Model Instability Across Edge Devices
We characterize different sources for instability, and show that differences in compression formats and image signal processing account for significant instability in object classification models.
Network Offloading Policies for Cloud Robotics: a Learning-based Approach
In this paper, we formulate a novel Robot Offloading Problem --- how and when should robots offload sensing tasks, especially if they are uncertain, to improve accuracy while minimizing the cost of cloud communication?
