  It is commonly observed that deep networks trained for classification exhibit
class-selective neurons in their early and intermediate layers. Intriguingly,
recent studies have shown that these class-selective neurons can be ablated
without deteriorating network function. But if class-selective neurons are not
necessary, why do they exist? We attempt to answer this question in a series of
experiments on ResNet-50s trained on ImageNet. We first show that
class-selective neurons emerge during the first few epochs of training, before
receding rapidly but not completely; this suggests that class-selective neurons
found in trained networks are in fact vestigial remains of early training. With
single-neuron ablation experiments, we then show that class-selective neurons
are important for network function in this early phase of training. We also
observe that the network is close to a linear regime in this early phase; we
thus speculate that class-selective neurons appear early in training as
quasi-linear shortcut solutions to the classification task. Finally, in causal
experiments where we regularize against class selectivity at different points
in training, we show that the presence of class-selective neurons early in
training is critical to the successful training of the network; in contrast,
class-selective neurons can be suppressed later in training with little effect
on final accuracy. It remains to be understood by which mechanism the presence
of class-selective neurons in the early phase of training contributes to the
successful training of networks.
