问题
When training a set of classes (let's say #clases (number of classes) = N) on Caffe Deep Learning (or any CNN framework) and I make a query to the caffemodel, I get a % of probability of that image could be OK.
So, let's take a picture of a similar Class 1, and I get the result:
1.- 90%
2.- 10%
rest... 0%
the problem is: when I take a random picture (for example of my environment), I keep getting the same result, where one of the class is predominant (>90% probability) but it doesn't belong to any class.
So what I'd like to hear is opinions/answers from people which has experienced this and would have solved how to deal with no-sense inputs to the Neural Network.
My purposes are:
- Train one more extra class with negative images (like with train_cascade).
- Train one more class extra with all the positive images in the TRAIN set, and the negative on the VAL set.
But my purposes don't have any scientific base to execute them, that's why I ask you this question.
What would you do?
Thank you very much in advance.
Rafael.
来源：https://stackoverflow.com/questions/31288156/convolutional-neural-networks-with-caffe-and-negative-images