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An introduction to one-shot learning

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Passport checks at airports and border gates present a special challenge: How do you tell if the person standing in front of you is the same person whose picture is in the passport? Border and customs officers solve this problem using the complex mechanisms ingrained in the human visual system through billions of years of evolution.

It’s not a perfect process, but it works well most of the time.

In the realm of artificial intelligence, this is called the “one-shot learning” challenge. In a more abstract way, can you develop a computer vision system that can look at two images it has never seen before and say whether they represent the same object?

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Data is one of the key challenges in deep learning, the branch of artificial intelligence that has had the most success in computer vision. Deep learning algorithms are notorious for requiring large amount of training examples to perform simple tasks such as detecting objects in images.

But interestingly, if configured properly, deep neural networks, the key component of deep learning systems, can perform one-shot learning on simple tasks. In recent years, one-shot learning AI has found successful applications including facial recognition and passport checks.

Classic convolutional neural networks

One of the most important architectures used in deep learning is the convolutional neural network (CNN), a special type of neural net that is especially good at working with visual data.

The classic use of CNNs is to set up multiple convolution layers (with some other important components in-between and after), specify an output goal, and train the neural network on many labeled examples.

For instance, an image classifier convnet takes an image as input, processes its pixels through its many layers, and outputs a list of values that represent the probability that the image belongs to one of the classes it detects.

alexnet CNN architecture