Compact representations and pruning in residual networks

20 Oct 2018  ·  Fereshteh Lagzi, Tonio Ball, Joschka Boedecker ·

We show that residual networks encode their input signals in the transient dynamics of the neurons in each layer. These representations are similar for inputs from the same class, and distinct for inputs from different classes. Based on the neural transient dynamics, we provide a sufficient criterion to determine the depth of such networks during training. This criterion is based on the convergence of the neural dynamics in the last two successive layers of the residual block. This method compresses the depth of the network and removes unnecessary deep layers.

PDF Abstract
No code implementations yet. Submit your code now

Tasks


Datasets


  Add Datasets introduced or used in this paper

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods


No methods listed for this paper. Add relevant methods here