Siamese Network in Keras

Question

Siamese Network in Keras

Peter

2022年1月5日 14:58

I‘m looking for a minimal applied example for the implementation of a (one shot) Siamese Network, preferably in Keras.

I‘m well aware of the various data science online pages and the respective examples and exercises that can be found there. However, so far I did not found an instructive source there.

I would be thankful if someone could point me to some github source or if someone could share some code or other sources, which provide a sound example on how to implement such an architecture in Python or in R.

Topic siamese-networks one-shot-learning

Category Data Science

Pluviophile · Accepted Answer · 2022年1月5日 14:58

From Official Keras examples:

Image similarity estimation using a Siamese Network with a triplet loss

Training a Siamese Network to compare the similarity of images using a triplet loss function.

A Siamese Network is a type of network architecture that contains two or more identical subnetworks used to generate feature vectors for each input and compare them.

Siamese Networks can be applied to different use cases, like detecting duplicates, finding anomalies, and face recognition.

This example uses a Siamese Network with three identical subnetworks. We will provide three images to the model, where two of them will be similar (anchor and positive samples), and the third will be unrelated (a negative example.) Our goal is for the model to learn to estimate the similarity between images.

Image similarity estimation using a Siamese Network with a contrastive loss

Similarity learning using a siamese network trained with a contrastive loss

Siamese Networks are neural networks that share weights between two or more sister networks, each producing embedding vectors of its respective inputs.

In supervised similarity learning, the networks are then trained to maximize the contrast (distance) between embeddings of inputs of different classes, while minimizing the distance between embeddings of similar classes, resulting in embedding spaces that reflect the class segmentation of the training inputs.