Inception Score calculation about improved-gan

[tsc2017]

Hi, I have rewritten the code for calculating Inception Score, taking the first problem into consideration:https://github.com/tsc2017/inception-score

As to the second problem, since the softmax function hardly outputs a 0 for a category, which means the conditional and marginal distribution of y is supported on all the 1000 classes, it is unlikely to get a 0*log(0), log(∞) or divide-by-0 error, and I do not observe any numerical instability, neither with the old nor with my new implementation.

Lastly, since the inception score is approximated by a statistic of a sample, just make sure the sample size is big enough. The common use of 50000 images in 10 splits seems acceptable. Take the CIFAR-10 training set images as an example, the inception score is around 11.34 in 1 split and 11.31±0.08 if in 10 splits.

[michaelklachko]

@lipanpeng https://arxiv.org/abs/1801.01973

@xunhuang1995 the third point is valid, because 5k splits might be too small to adequately represent 1k classes. And as they show in the paper, IS changes depending on the size of the split.

[xunhuang1995]

The first point is an important issue. For the third point, note that they do NOT intend to calculate the inception score (IS) for 50,000 inputs. Rather, they spit 50,000 samples into 10 splits each with 5,000 samples. They then calculate IS for each split and return the average IS over splits. So the code is correct.

[mathfinder]

So,which version is correct？

[lipanpeng]

Can anyone tell me where can I find the material about the inception score?