DEEP FEATURE COMPRESSION WITH SPATIO-TEMPORAL ARRANGING FOR COLLABORATIVE INTELLIGENCE

Collaborative Intelligence is a new paradigm that splits a deep neural network~(DNN) into an edge DNN and a cloud DNN. In this paradigm, deep features, which are the outputs of the edge DNN, are compressed and transmitted to the cloud DNN. Since the deep features have a few responses that are similar to each other, previous studies have proposed compressing them as an image with a spatial arrangement to utilize spatial correlation between the deep features. However, this method may not sufficiently consider the similarity because only the spatial correlation is utilized. In this work, we propose a ``spatio-temporal arranging'' that considers the similarity of deep features in both the spatial and temporal directions. This method arranges the deep features spatio-temporally and compresses them as a video to utilize the spatio-temporal correlation. We perform spatial arranging as images to increase the spatial correlation and temporal arranging as a video with a novel ordering search to increase the temporal correlation. Experimental results demonstrate that our method performs better than previous methods.