  Modern image files are usually progressively transmitted and provide a
preview before downloading the entire image for improved user experience to
cope with a slow network connection. In this paper, with a similar goal, we
propose a progressive transmission framework for deep learning models,
especially to deal with the scenario where pre-trained deep learning models are
transmitted from servers and executed at user devices (e.g., web browser or
mobile). Our progressive transmission allows inferring approximate models in
the middle of file delivery, and quickly provide an acceptable intermediate
outputs. On the server-side, a deep learning model is divided and progressively
transmitted to the user devices. Then, the divided pieces are progressively
concatenated to construct approximate models on user devices. Experiments show
that our method is computationally efficient without increasing the model size
and total transmission time while preserving the model accuracy. We further
demonstrate that our method can improve the user experience by providing the
approximate models especially in a slow connection.
