When understanding the inception module, I once saw the following statement from an online post. What's the calculation underline the "
192 28×28 sized feature maps can be reduced to 64 28×28 feature maps through 64 1×1 convolutions"
Inception modules in convolutional networks were designed to allow for deeper and larger convolutional layers while at the same time allowing for more efficient computation. This is done by using 1×1 convolutions with small feature map size, for example, 192 28×28 sized feature maps can be reduced to 64 28×28 feature maps through 64 1×1 convolutions.