I have a large database of images that are only partially labeled for multiple, non-exclusive characteristics or objects present on them. For instance, an underwater scene might feature the labels
fish on it.
Problem is it's only partially labeled, meaning that the fact the label
cat is absent doesn't mean there's no cat on the image.
What'd be the best strategy to train a CNN able to tell the missing labels on the dataset?
The image base has 230 thousand images on it, but given that multiple labels are possible, it's hard to tell the ratio of images that are fully labeled. I'm able to code python and managed to use keras to train on mnist dataset on gpu.