Obtain negative results from a machine learning algorithm

Question

I have a set of images of a particular object. I want to find if some of these has anomalies with a machine learning algorithm. For example if I have many photos of glasses I want to find if one of these is broken or has something anomalous. Something like this:

GOOD!!

BAD!!

(Obviously I will use the same kind of glasses...)

The problem is that I don't know every negative situation, so, for training, I have only positive images.

In other words I want an algorithm that recognize if an image has something different from the dataset. Do you have any suggestion?

In particular is there a way to use convolutional neural network?

Answer 1

What you are looking for is usually called anomaly, outlier, or novelty detection. You have lots of examples of what your data should look like, and you want to know when something doesn't look like your data.

A good approach for this problem, since you are using images, you can get a feature vectorized version using a pre-trained CNN on image net. Then you can use an anomaly detector on that feature set. The isolation forest should be an easier one to get working.