  The internal states of most deep neural networks are difficult to interpret,
which makes diagnosis and debugging during training challenging. Activation
maximization methods are widely used, but lead to multiple optima and are hard
to interpret (appear noise-like) for complex neurons. Image-based methods use
maximally-activating image regions which are easier to interpret, but do not
provide pixel-level insight into why the neuron responds to them. In this work
we introduce an MCMC method: Langevin Dynamics Activation Maximization (LDAM),
which is designed for diagnostic visualization. LDAM provides two affordances
in combination: the ability to explore the set of maximally activating
pre-images, and the ability to trade-off interpretability and pixel-level
accuracy using a GAN-style discriminator as a regularizer. We present case
studies on MNIST, CIFAR and ImageNet datasets exploring these trade-offs.
Finally we show that diagnostic visualization using LDAM leads to a novel
insight into the parameter averaging method for deep net training.
