Supplementary Material : Switching Convolutional Neural Network for Crowd Counting

Differential training on the CNN regressors R1 through R3 generates a multichotomy that minimizes the predicted count by choosing the best regressor for a given crowd scene patch. However, the trained switch is not ideal and the manifold separating the space of patches is complex to learn (see Section 5.2 of the main paper). To mitigate the effect of switch inaccuracy and inherent complexity of task, we perform coupled training of switch and CNN regressors. We ablate the effect of coupled training by training the switch classifier in a stand-alone fashion. For training the switch in a stand-alone fashion, the labels from differential training are held fixed throughout the switch classifier training. The results of the ablation are reported in Table 1. We see that training the switch classifier in a stand-alone fashion results in a deterioration of Switch-CNN crowd counting performance. While Switch-CNN with the switch trained in a stand-alone manner performs better than MCNN, it performs significantly worse than Switch-CNN with coupled training. This is reflected in the 13 point higher count MAE. Coupled training allows the patch labels to change in order to adapt to the ability of the switch classifier to relay a patch to the optimal regressor Rk correctly. This co-adaption is absent when training switch alone leading to deterioration of crowd counting performance.