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This week you'll learn about Training and Making Predictions with Siamese Networks and Triplet Loss.

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In recent years, deep learning techniques have gained a lot of attention and popularity in computer vision, particularly for tasks like image recognition and similarity matching. Siamese networks with triplet loss are one such technique widely used for training models to perform these tasks.

In summary, Siamese networks are neural networks of two identical subnetworks with the same weights and architecture. These networks are trained on pairs of input data, aiming to learn a similarity metric that can distinguish between similar and dissimilar data points. On the other hand, triplet loss is a function commonly used in conjunction with Siamese networks to train them for similarity-matching tasks. It involves minimizing the distance between an anchor and a positive example while maximizing the distance between the anchor and a negative example.

We have been building toward a full-fledged face recognition pipeline throughout this series. We are finally at the juncture where we have all the necessary pieces ready, and all we need to do is build suitable connecting wires for our pipeline to be ready. 

The big picture: We have long since established that Siamese networks are a type of neural network that excels at tasks like image recognition, facial recognition, and text matching. Moreover, they become a powerful tool for training and making predictions when combined with triplet loss. This blog post serves as the step in which we bring all we have learned and built together throughout this series.

How it works: To finalize our pipeline, we implement the steps required to complete our face recognition application (i.e., model training, validation, and testing). Since most of the pipeline was already built by us in the previous blog posts of this series, our task here is fairly simple. 

Our thoughts: Our series systematically attempts to demystify Siamese Networks with Triplet Loss and apply them to an image recognition task. The step of connecting all the necessary wires is vital. 

Yes, but: Although Siamese Networks are powerful tools to boost your image recognition tasks, they come with certain limitations (e.g., training complexity, limited scalability, difficulty in handling complex data relationships, etc.). However, despite these limitations, it is still acknowledged for its effectiveness, and researchers are actively working on developing solutions for them. 

Stay smart: If you made it this far with us, see it through! The next part of this series deals with specific evaluation metrics like ROC, Precision, and Recall. As a deep learning practitioner, all your hard work depends on how well your trained model performs on task-specific objective functions. So, don't miss out on it!

Click here to read the full tutorial

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P.S. Join us when we go live with HuggingFace to talk MaskFormer on Thursday, March 23 at 9:30 AM ET: