Zero-shot Learning, Explained


How you can train a model to learn and predict unseen data?

The reason why machine learning models in general are becoming smarter is due to their dependency on using labeled data to help them discern between two similar objects.

However, without these labeled datasets, you will encounter major obstacles when creating the most effective and trustworthy machine-learning model. Labeled datasets during the training phase of a model are important.

Deep learning has been widely used to solve tasks such as Computer vision using supervised learning. However, as with many things in life, it comes with restrictions. Supervised classification requires a high quantity and quality of labeled training data in order to produce a robust model. This means that the classifying model cannot handle unseen classes.

And we all know how much computational power, re-training, time, and money it takes to train a deep learning model.

But can a model still be able to discern between two objects without having used training data? Yes, it’s called zero-shot learning. Zero-shot learning is a model’s ability to be able to complete a task without having received or used any training examples.

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