Understanding Zero-Shot Learning — Making ML More Human

We use cookies to enhance your user experience. Clicking 'Agree' indicates your consent to the use of cookies. Zero-shot learning allows a model to recognize what it hasn’t seen before. Imagine you’re tasked with designing the latest and greatest machine learning model that can classify all animals. Yes, all animals. Using your machine learning knowledge, you immediately understand that we need a labeled dataset with at least one example for every single animal. There’s 1,899,587 described species in the world, so you’re gonna need a dataset with roughly 2 million different classes. Yikes. As you’ve probably noticed by now, getting large quantities of high quality labeled data is hard. Very hard. It doesn’t help when there are a gazillion different classes (i.e. animal species) that your model has to learn. So how do we solve this problem? One way is to decrease our models’ reliance on labeled data. This is the motivation behind zero-shot learning, in which your model learns how to cla

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• Aryan Garg