loss is negative

[jlian2]

I just run a demo program, in which I would love to perform on these data: Actually, blue points are real data(x) while red points are z. The prior is N(mean of x, 0.01). Notice that I randomly set variance. Maybe 0.01 is larger than variance of x. Maybe smaller. I apply the default flows model in your ipynb. But loss varied from 800000->-30000->, and it is still decreasing. My question is how come negative loss would happen? Plu, when I perform MAF/IAF,,,, loss would also be negative

[jlian2]

To complement, As you can see, when loss goes negative, loss still decreases. But results seem to be better?

[gideonite]

Hi @jlian2, I've been playing around with the code in the repo as well.

• I've also observed negative loss values. Since the loss is the negative log-likelihood, in fact what this should mean is that the likelihood is enormous. I think this means that it is essentially very peaked. This may correspond to overfitting. I've found that you can often get good results if you stop the training early.

• It's hard to get a sense for the dataset that you are working with. Is it essentially a Gaussian with outliers? It wouldn't surprise me in flows with a Gaussian base distribution would not handle outliers well. Maybe this is a direction for future research?

• Have you tried other flow models? Glow?