An Investigation of the Weight Space for Version Control of Neural Networks.

Deployed Deep Neural Networks (DNNs) are often trained further to improve in performance. This complicates the tracking of DNN model versions and the synchronization between already deployed models and upstream updates of the same architecture. Software Version Control cannot be applied straight-forwardly to DNNs due to the different nature of software and DNN models. In this paper we investigate if the weight space of DNN models contains a structure, which can be used for the identification of individual DNN models. Our results show that DNN models evolve on unique, smooth trajectories in weight space which we can exploit as feature for DNN version control.