Monday, February 13, 2023

New Tutorial: Building a Dataset for Triplet Loss with Keras and TensorFlow

Hi,

This week you'll learn about Building a Dataset for Triplet Loss with Keras and TensorFlow.

After years of training in the art of deep learning, you have finally achieved your dream job! A researcher at a top-notch AI lab. On your first day, outside the laboratory, you see a device scan your face and let you in. This takes you back to your formative years, when you were diving into deep learning projects left and right. The mighty face scanner successfully protects the lab from any outsiders, yet you marvel at how simple it is to create!

When you think about it, a single picture of your face can be easily identified by a well-trained neural network. But what if it's your face with a different expression? A single instance might not be enough for your model to learn your face's features given any circumstances successfully. To make the process far more robust, not only does your model need to learn more instances of your face, but it also needs to understand why it is different from other faces.

For that, we need to create the right dataset to feed into our model. That is exactly what this blog post is all about!

The big picture: Siamese networks and contrastive loss can be extremely effective if used correctly. However, the nature of the network is such that feeding a normal dataset without prior preparation won't do the task. For that reason, we build our own dataset for triplet loss.

How it works: As mentioned before, the aim is to teach the model how a face is similar to another instance of its own (positive pair) as well as how a face is different from another face (negative pair). This is exactly how we have to create our data instances. We will have 3 images: one of them being the anchor, one being the positive image, and the final image being the negative image.

Our thoughts: Learning to build a triplet loss dataset is integral to understanding how to use Siamese architectures best.

Yes, but: Don't slack off after this. The best part of this series is yet to come!

Stay smart: Try this out yourself!

Click here to read the full tutorial

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