Siamesenätverk

Siamesenätverk, sometimes referred to as Siamese networks, are a class of neural networks used in machine learning for tasks involving comparison. Their primary purpose is to determine the similarity or dissimilarity between two input samples. This is achieved by processing each input through identical (or very similar) neural network pathways, called twin networks.

The core idea behind a Siamese network is that if two inputs are similar, their processed representations (embeddings) generated by the twin networks will be close in the embedding space. Conversely, if the inputs are dissimilar, their embeddings will be far apart. The networks are trained using pairs of data, labeled as either similar (positive pairs) or dissimilar (negative pairs). A contrastive loss function is commonly employed during training to pull similar pairs closer together and push dissimilar pairs further apart in the embedding space.

Siamese networks have found applications in various fields. In facial recognition, they can determine if two images depict the same person. In signature verification, they can authenticate whether a given signature matches an original. They are also used in plagiarism detection, recommendation systems, and even in determining the similarity of drug molecules. The flexibility of the twin network architecture allows it to be adapted to different data modalities, including images, text, and time series.