A Siamese Network is a type of neural network architecture characterized by having two or more identical subnetworks (or 'towers') that share the same weights and architecture. Its primary purpose is to learn a similarity metric between two inputs, rather than performing direct classification. The core mechanism involves feeding two distinct inputs into the twin networks, which then produce corresponding feature vectors in an embedding space. A distance metric (e.g., Euclidean distance) or a contrastive loss function (like triplet loss or contrastive loss) is applied to these feature vectors to minimize the distance between similar pairs and maximize it for dissimilar pairs. This architecture is crucial for tasks where the number of classes is very large or changes frequently, or when only a few examples per class are available (few-shot learning). It is widely used in research areas like computer vision for face recognition, signature verification, and object tracking, as well as in natural language processing for semantic similarity and in graph learning for node classification, as seen in methods like SNGCL.
Siamese Networks are neural networks with two identical branches that learn to compare inputs and determine their similarity. They are particularly useful for tasks where you need to tell if two items are the same or different, even with limited examples, by mapping them to a space where similar items are close together.
Twin Network, Siamese Neural Network, Pseudo-Siamese Network, Triplet Network
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