How so? Overfitting just means that the model is fitting noise in the training set, instead of the broad patterns of interest. (With a sufficiently generous de

How so? Overfitting just means that the model is fitting noise in the training set, instead of the broad patterns of interest. (With a sufficiently generous definition of "noise" that includes "the random process that selected training data from an underlying distribution".) Just seeing recognizable images in a visualization of the model doesn't tell you anything about overfitting. As an extreme example, a k-nearest-neighbors classifier is a model that consists of nothing except verbatim examples from the training set, but that doesn't mean it's always overfit. Plus, it's not immediately obvious that the image you're seeing is actually memorized from the training set. Maybe the model is just really good at synthesizing plausible-looking sheepdog images from random noise.