The double exposure (blended) photos displayed above are 100% machine generated with no human involvement. The program picks a random human photo and combines it with a random background photo. But, this current version does not use AI. We did try using a machine learning model to have it display only "good" photos, but we were not able to get it to work better than randomly blending the photos. There are 2 problems with using AI for it. First, what is considered a good photo blend is very subjective. We classified demo blends into good and bad categories to train the neural network model, but other humans might come up with totally different groups of good and bad photos. And, there is a difference between measuring the quality of the double exposure blend, and the overall quality of the final image. If one of the original photos was amazing, it is much more likely the blend will still produce a very good image. For example, if you can hardly see the second photo in the blend, and the first photo looks great by itself, would that still be good? In the end, all art is subjective.
The second, and much bigger problem, was that when we trianed the machine learning model, it did not know it was looking at double exposure images. It treats this task the same, for example, as if it was trying to learn how to recognize dogs vs cats. It looks at the photos in each category and tries to learn the difference. But it is not really looking at the blending part, because it has no idea what that is. As an example, if the "good" training set happened to have more photos with oceans/lakes in it, then the ML model would consier almost any photo with an ocean or lake in it to be a good photo. But that really has nothing to do with photo blending or even the quality of the photo.
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