Siamese networks are a type of neural network architecture that is particularly suited for self-supervised learning tasks, especially those involving similarity or distance measurement between data points. The key aspect of a Siamese network is that it consists of two identical subnetworks that share the same weights and parameters. These subnetworks process two separate inputs simultaneously and output feature vectors, which can then be compared using a distance metric, such as Euclidean distance or cosine similarity. In self-supervised learning, where labeled data is scarce or unavailable, Siamese networks can learn representations by predicting the similarity or dissimilarity between unlabeled data pairs.
For example, consider a task where you want to learn features from images without having labeled datasets. You can create pairs of images where some pairs are similar (e.g., two pictures of the same dog) and others are dissimilar (e.g., a picture of a dog and a car). The Siamese network processes these image pairs, computing feature representations for each. During training, the network is optimized to minimize the distance between the feature vectors of similar pairs while maximizing the distance for dissimilar pairs. This way, the network learns rich representations of the input data, which can be useful for various downstream tasks like image classification or clustering, even without any labeled examples.
The application of Siamese networks in self-supervised learning sets the stage for tasks beyond image processing. For instance, they can be utilized in natural language processing (NLP) where the goal might be to assess semantic similarity between sentences. By training on pairs of sentences and adopting a similar approach of minimizing distance for semantically similar pairs and maximizing it for dissimilar ones, the network becomes adept at understanding contextual relationships. This flexibility illustrates how Siamese networks can efficiently harness self-supervised learning paradigms across diverse fields, empowering developers to work with unlabelled data effectively.