15.8% accuracy != singularity is near.
No more supplements-eating Kurzweil, walking Terminators and Skynet-like BS please.
No more supplements-eating Kurzweil, walking Terminators and Skynet-like BS please.
Then, what kind of thing are you measuring? Recognizing patterns ? We know humans are very good at that. We can see thousands of different people everyday but we can recognize in the blink of an eye a familiar face. A computer or computer network is very, very, very far from being able to do that yet.
edit: this quote puts things into perspective a bit "It is worth noting that our network is still tiny compared to the human visual cortex, which is 1,000,000 times larger in terms of the number of neurons and synapses."
A human baby learns from uncleaned raw data using far less energy with better generalization than a computer and fuses large amount of data without suffering from dimensionality curses.
I think it is safe to say that human babies are still ahead. for now.
In a way their first input processing of the real images was pixelisation filter. If you feed pixelised image to a person you see how much information is lost. If you make single pixels occupy significant portion of view person might loose ability to recognize the image at all. Feeding so little information to CV system is like trying to teach nearly blind man to see.
To improve CV we should focus on finding best ways of converting full resolution visual data to something of smaller volume in such way that important features are preserved. This input data IMO should also include time. I, thanks to crappy eyesight often recognize people, actions, objects relying more on how they move not how they look to me. Even with sharp eysight sometime your vision just gets stuck and can't recognize what is in the scene you are currently looking at. You can't understand what you see until something in the scene moves or you move a bit.
You are exactly right! See Dictionary Learning, Random projection, compressive sensing. As for time, perhaps you are right I don't know. That question is: would a suitably written video trained classifier that preserved temporal features do better on image classification?
Seeing is in my opinion very similar to understanding language in sense that the information that is transferred, observed image or words heard are just small fuzzy fragments. Sender (speaker, or in case of vision, physical world) has rich model and recipient has rich model of all the things they can communicate about and the actual information passed, only indicates the parts of the underlying model to the recipient, that he should select and how he should modify them to get the message.
Building usable model from small fuzzy fragments of information that are passed when recognizing image or hearing spoken words should be incredibly hard task and I think no biological brain could do that. I think that absorbing as much real information as possible at the time of training the classifier is absolutely crucial for achieving anything close to what humans or animals can do.
From the techniques you mentioned, dictionary learning looks most awesome to me, and most applicable to CV.