Siamese Neural Networks

2019-10-25T11:20:16+02:00Categories: Deep Learning|Tags: , , |1 Comment

Siamese networks were first introduced by Bromley and LeCun [1] in early 1990s to solve signature verification as an image matching problem. A similar Siamese architecture was independently proposed for fingerprint identification by Baldi and Chauvin [2] in 1992.  Later in 2015, Gregory Koch et al. [3] proposed to use Siamese neural networks for one-shot image recognition. Siamese neural networks are designed as two twin networks that are connected by their final layer by means of a distance layer that is trained to predict whether two images belong to the same category or not. The networks that compose the siamese architecture are called twins because all the weights and biases are tied, which means that both networks are symmetric. Symmetry is important as the network should be invariant to switching the input images. Moreover, this characteristic makes the networks much faster to train since the number of [...]