  The field of machine learning has been greatly transformed with the
advancement of deep artificial neural networks (ANNs) and the increased
availability of annotated data. Spiking neural networks (SNNs) have recently
emerged as a low-power alternative to ANNs due to their sparsity nature. In
this work, we propose a novel hybrid ANN-SNN co-training framework to improve
the performance of converted SNNs. Our approach is a fine-tuning scheme,
conducted through an alternating, forward-backward training procedure. We apply
our framework to object detection and image segmentation tasks. Experiments
demonstrate the effectiveness of our approach in achieving the design goals.
