In-situ animal behavior classification using knowledge distillation and fixed-point quantization. (arXiv:2209.04130v1 [cs.LG])

We explore the use of knowledge distillation (KD) for learning compact and
accurate models that enable classification of animal behavior from
accelerometry data on wearable devices. To this end, we take a deep and complex
convolutional neural network, known as residual neural network (ResNet), as the
teacher model. ResNet is specifically designed for multivariate time-series
classification. We use ResNet to distil the knowledge of animal behavior
classification datasets into soft labels, which consist of the predicted
pseudo-probabilities of every class …

arxiv behavior classification distillation fixed-point knowledge quantization