You need to know two important concepts to get through this lesson. You need to know that “F” stands for Finite Automata and you need to know that in real life we tend to have higher and lower bounds on F. The idea that we’re used to a constant lower bound on the number of events we can detect per second is inaccurate because there are a vast array of possible computations within our sensory cortex.
I’ll assume you know the basics of probabilistic machine learning, which is what I’ll use for the rest of this post as well. Probabilistic machine learning is a powerful technique in computer vision and machine learning. It has been used successfully to reduce noisy visual cues with low false positive rate. However, there is the problem that noisy inputs can cause an exponential increase in F and this exponential increase may also produce false positives if multiple detectors are combined within a single model, or if a particular combination of detectors is used.
In our case, the idea is to create a system which learns to detect when each of the event objects have a certain number of events. Since all of these are event objects in our case we can also think of each as a type of detector: a detector that counts events without actually having a detector to detect them and so these are referred to as detectors.
The idea of using a system that is learning to detect an event object in a probabilistic way without actually detecting any event objects is called deep learning. The idea of using two separate detectors to detect a particular event is called supervised deep learning. Here is a screen shot of the output of the SVM training code after training for 50 epochs when running on a GPU in my system.
Notice that even though I have 100 events per pixel I still need to feed in more data to build a model which can detect these event objects. The output in the middle of the screen is where my model has a lot of false positive rate. The model is not perfect and it’s definitely not 100% accurate, but I still learned from it. That’s the point of deep learning!
Summary of our training data
Ok thanks for asking. There will be a more detailed training of my model on my blog soon, but here is what I have learned so far about a probabilistic object detector. My model still shows the same pattern of false positives on the screen:
This is a common pattern and seems similar to how visual signals were detected by the SVM. That pattern
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