The Inception score is a heuristics-based method to measure the quality and diversity of fake images. The score uses a pretrained neural network for image classification, called the Inception network. It was first proposed in the paper, Improved Techniques for Training GANs.
The main novelties in Inception networks are the addition of 1 x 1 convolution layers and the global average pooling layers with multiple convolutions (1 x 1, 3 x 3, and 5 x 5), and a 3 x 3 max pooling layer.
Whereas the 1 x 1 convolution layers are a weighted combination of all the input channels at the current layer, the global average pooling layer improves the robustness of spatial translation and replaces dense layers, thus decreasing the number ...
