A Siamese network is a neural architecture that uses twin subnetworks to process two inputs and compare their outputs. In self-supervised learning (SSL), this setup enables models to learn representations by identifying relationships between augmented or modified versions of the same data without relying on labeled examples. The core idea is to train the network to recognize similarities or differences between inputs derived from the same source, using the inherent structure of the data itself as supervision. For example, two crops of an image or text segments from a document can be fed into the twin networks, and the model learns to produce similar embeddings for related inputs and dissimilar ones for unrelated pairs.
A key application of Siamese networks in SSL is contrastive learning frameworks like SimCLR or MoCo. In SimCLR, for instance, the network processes two augmented views (e.g., cropped, rotated, or color-adjusted versions) of the same image through identical subnetworks. The training objective forces the embeddings of these views to align while pushing apart embeddings from different images. The Siamese structure ensures the subnetworks share weights, guaranteeing consistent feature extraction across both augmented inputs. This approach eliminates the need for manual labels by using the natural variations in the data as the supervisory signal. Similarly, in NLP, models like Sentence-BERT use Siamese networks to learn sentence embeddings by comparing pairs of semantically similar or dissimilar text snippets.
Beyond contrastive methods, Siamese networks are also used in non-contrastive SSL approaches like BYOL (Bootstrap Your Own Latent). Here, one subnetwork (the “online” network) learns to predict the output of a slowly updated “target” network processing a different augmented view of the same input. Though no explicit negative samples are used, the Siamese structure enforces consistency between the two subnetworks’ outputs. Developers can implement these concepts using frameworks like PyTorch or TensorFlow by defining twin networks with shared weights and designing loss functions that operate on their combined outputs. For example, a cosine similarity loss might compare embeddings from the two subnetworks, while a predictor head (as in BYOL) could bridge their outputs. The critical takeaway is that Siamese networks provide a flexible way to leverage data relationships in SSL, making them a foundational tool for representation learning without labeled data.