Question 3
In Inception/GoogLeNet modules, the main purpose of 1×1 convolutions is to:
Increase the receptive field of subsequent convolutional layers without downsampling.
Reduce channel dimensionality (bottleneck) and add non-linearity
Act as a differentiable substitute for max-pooling operations to preserve exact spatial hierarchies.
Project the feature maps into a higher-dimensional space to separate entangled features.