Post-Train Adaptive MobileNet for Fast Anti-Spoofing
Kostiantyn Khabarlak · 2022
Many applications require high accuracy of neural networks, as well as low latency and user data privacy guaranty. Face anti-spoofing is one of such tasks. However, a single model might not give the best results for different device performance categories, while training multiple models is time consuming. In this work we present Post-Train Adaptive (PTA) block. Such a block is simple in structure and offers a drop-in replacement for MobileNetV2 Inverted Residual block. PTA block has multiple branches with different computation costs. The branch to execute can be selected on-demand and at runtime, thus offering different inference times and configuration capability for multiple device tiers. Crucially, the model is trained once and can be easily reconfigured after training, even directly on a mobile device. In addition, the proposed approach shows substantially better overall performance in comparison to the original MobileNetV2 as tested on CelebA-Spoof dataset. Different PTA block configurations are sampled at training time, which also decreases overall wall-clock time needed to train the model. While we present computational results for the anti-spoofing problem, the MobileNetV2 with PTA blocks is applicable to any problem where the original MobileNetV2 was, which makes the results presented practically significant. Keywords 1 Neural Network Adaptation, Post-Train Adaptive, Inference Speed, Mobile Computing, Edge Computing, Anti-Spoofing, Computer Vision