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  • 1.
    Carlsson, Stefan
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Azizpour, Hossein
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Razavian, Ali Sharif
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Sullivan, Josephine
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Smith, Kevin
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    The Preimage of Rectifier Network Activities2017In: International Conference on Learning Representations (ICLR), 2017Conference paper (Refereed)
    Abstract [en]

    The preimage of the activity at a certain level of a deep network is the set of inputs that result in the same node activity. For fully connected multi layer rectifier networks we demonstrate how to compute the preimages of activities at arbitrary levels from knowledge of the parameters in a deep rectifying network. If the preimage set of a certain activity in the network contains elements from more than one class it means that these classes are irreversibly mixed. This implies that preimage sets which are piecewise linear manifolds are building blocks for describing the input manifolds specific classes, ie all preimages should ideally be from the same class. We believe that the knowledge of how to compute preimages will be valuable in understanding the efficiency displayed by deep learning networks and could potentially be used in designing more efficient training algorithms.

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