The question of single unit semantics in deep networks. Do we have reason to be a little less bewildered by so-called grandmother cells in the context of deep neural networks?
A familiar hypothesis in deep learning is that a single higher-layer unit of a network may correspond to a complex semantic entity, such as a person or a specific type of dog [2, 9]. This has colloquially been referred to as grandmother cells or Homer Simpson cells.
These names stem from neuroscience. It has been observed for the human brain that specific cortical cells in late stages of the visual pathway can activate when a person is shown any variant of a photograph of, for instance, Jennifer Aniston, as was done in the study by Quiroga et al. . In the study, however, it was also found that the same response could be elicited by the printed name of Jennifer Aniston, indicating that the firing resulted from memory mechanisms rather than pure visual stimuli and geometric transformations thereof.
The concept of grandmother cells has made an impression on the broad community of practitioners in deep learning, being both captivating and easy to grasp. Visualization methods of these cells have been presented, both network-based [2,7] and input-based .
In deep learning papers, it is not uncommon to show these kinds of visualizations and assume that they are proof that the network has learned something sensible, e.g., .
Whether this really is the case is what I intend to investigate in this blog post. For this purpose, I will mainly refer to three papers that represent three different perspectives on the matter. They are Visualizing and understanding convolutional neural networks by Zeiler & Fergus , Salient deconvolutional networks by Mahendran & Vedaldi , and On the importance of single directions for generalization by Morcos & Barrett .
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