# What is Siamese neural networks

##### Understanding Siamese Neural Networks (SNNs)

Siamese Neural Networks (SNNs), first introduced by Bromley et al. in 1994, are a special type of neural networks that are primarily used for solving problems that require finding similarity or dissimilarity between two data points. The architecture of Siamese Neural Networks comprises two identical subnetworks that are trained with the same parameters and weights.

The output of each of these two subnetworks is then compared using a specific distance metric, such as Euclidean or Manhattan distance, to determine the similarity or dissimilarity between the two input data points.

SNNs have found many applications in fields such as computer vision, natural language processing, and speech recognition. In this article, we will take a look at how Siamese Neural Networks work, their advantages, and some applications where they can be used effectively.

##### How Do Siamese Neural Networks Work?

In Siamese Neural Networks, two identical neural networks are trained with the same set of input data points. The input data for both these networks can be of varying types such as text, images, audio, or any other form of data. Once the training is complete, the two subnetworks are merged together, and the similarity or dissimilarity between the two input data points is determined.

The most important component of a Siamese Neural Network is its distance metric. The distance metric measures the similarity or dissimilarity between two input data points by computing the distance between their respective embeddings. The embeddings are a compact representation of the input data, which are generated by the same subnetworks during the training phase. The most commonly used distance metrics are Euclidean distance, Manhattan distance, and Cosine similarity.

The embeddings generated by the Siamese Neural Network are fed into a distance metric function that computes the distance between them. If the distance between the embeddings is small, then the two input data points are considered similar, while if the distance is large, then they are considered dissimilar.

One advantage of using Siamese Neural Networks is that they enable learning from less data. This is because Siamese Networks can learn to identify similarities or dissimilarities between two data points with only a few examples. This makes them very useful in applications where only a small amount of data is available, such as in medical diagnosis or fraud detection.

##### Advantages of Siamese Neural Networks

Now that we have an understanding of how Siamese Neural Networks work, let's take a look at some of their advantages:

• Efficient Learning: As mentioned earlier, Siamese Neural Networks can learn from only a small number of examples. This makes them very efficient in applications where data is limited.
• Robustness: SNNs are quite robust and can handle noisy or incomplete data. They also work well with data that is not well-structured.
• Scalability: Siamese Neural Networks can also be scaled up to handle very large datasets while maintaining accuracy.
• High Accuracy: Due to their ability to learn similarity or dissimilarity between two data points, Siamese Neural Networks can achieve high accuracy in applications such as image recognition and natural language processing.
##### Applications of Siamese Neural Networks

Now that we know the advantages of Siamese Neural Networks, let's look at a few applications where they can be used effectively:

• One-Shot Learning: Siamese Neural Networks can be used for one-shot learning in applications such as face recognition. The network can identify a person with only one image and use that image as a reference to recognize the same person in other images.
• Image Retrieval: SNNs can also be used for image retrieval, where the network can find similar images in large datasets. This is especially useful in image-based search engines or stock photo websites.
• Text Classification: In natural language processing, Siamese Neural Networks can be used for text classification, where the network can identify similar or dissimilar text based on semantic similarity.
• Medical Diagnosis: Siamese Neural Networks can also be used for medical diagnosis, where the network can identify similarities between symptoms and medical conditions to help doctors make accurate diagnoses.
##### Challenges of Siamese Neural Networks

While Siamese Neural Networks have many advantages, they also face some challenges that need to be addressed:

• Data Augmentation: Siamese Neural Networks require a lot of data during the training phase. Data augmentation techniques can be used to generate more data from a limited dataset.
• Optimization: Siamese Neural Networks are often difficult to optimize due to the many hyperparameters involved. This can lead to overfitting or underfitting of the model.
• Interpretability: Siamese Neural Networks can be difficult to interpret, making it difficult to determine how the network arrived at its conclusions.
##### Conclusion

Siamese Neural Networks are a powerful tool for solving problems that require finding similarity or dissimilarity between two data points. They are efficient, scalable, and can achieve high accuracy in applications such as image recognition and text classification. While there are some challenges associated with using Siamese Neural Networks, they are quickly becoming a popular tool for many industries due to their ability to learn from less data. As research in this area continues, it is likely that we will see even more applications for Siamese Neural Networks in the future.