  Neural networks are largely black boxes. A neural network trained to classify
fruit may classify a picture of a giraffe as a banana. A neural network
watchdog's job is to identify such inputs, allowing a classifier to disregard
such data. We investigate whether the watchdog should be separate from the
neural network or symbiotically attached. We present empirical evidence that
the symbiotic watchdog performs better than when the neural networks are
disjoint.
