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Siamese networks are a type of neural network architecture that contains two or more identical subnetworks. They are used to find the similarity of inputs by comparing their feature vectors. This allows Siamese networks to learn a similarity function and classify new classes of data without retraining, which is useful for problems like face recognition that require learning from limited data. The main advantages are that they are more robust to class imbalance and can learn from semantic similarity with only a few images per class. However, their drawbacks include needing more training time than normal networks and being slower than normal classification learning.

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SIAMESE NETWORKS Dr.M.VidhyaLakshmi SRMIST,RMP SRM Institute of Science and Technology 1
Siamese Networks ● Neural networks rely on more data to perform well. ● Problems like face recognition and signature verification, need more data.To solve these tasks,we have a new type of neural network architecture called Siamese Networks. ● Only a few numbers of images is required to get better predictions. ● It can learn from very little data .Siamese networks more popular in recent years.
● A Siamese Neural Network is a class of neural network architectures which contains two or more identical subnetworks. ‘identical’ means, they have the same configuration with the same parameters and weights. ● Parameter updating is mirrored across both sub-networks. ● It is used to find the similarity of the inputs by comparing its feature vectors, so these networks are used in many applications ● Traditionally, a neural network learns to predict multiple classes. This poses a problem when we need to add/remove new classes to the data. ● In this case, we have to update the neural network and retrain it on the whole dataset. Also, deep neural networks need a large volume of data to train on. ● SNNs, on the other hand, learn a similarity function. Thus, we can train it to see if the two images are the same (which we will do here). This enables us to classify new classes of data without training the network again.
Pros and Cons of Siamese Networks: The main advantages of Siamese Networks are, ● More Robust to class Imbalance: a few images per class is sufficient for Siamese Networks to recognize those images in the future ● Nice to an ensemble with the best classifier: ● Learning from Semantic Similarity
The drawback of the Siamese Networks are, ● Needs more training time than normal networks ● It is slower than normal classification type of learning ● It won’t output the probabilities of the prediction, but the distance from each class

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Siamese networks.pptx.pdf

  • 2. Siamese Networks ● Neural networks rely on more data to perform well. ● Problems like face recognition and signature verification, need more data.To solve these tasks,we have a new type of neural network architecture called Siamese Networks. ● Only a few numbers of images is required to get better predictions. ● It can learn from very little data .Siamese networks more popular in recent years.
  • 3.
  • 4. ● A Siamese Neural Network is a class of neural network architectures which contains two or more identical subnetworks. ‘identical’ means, they have the same configuration with the same parameters and weights. ● Parameter updating is mirrored across both sub-networks. ● It is used to find the similarity of the inputs by comparing its feature vectors, so these networks are used in many applications ● Traditionally, a neural network learns to predict multiple classes. This poses a problem when we need to add/remove new classes to the data. ● In this case, we have to update the neural network and retrain it on the whole dataset. Also, deep neural networks need a large volume of data to train on. ● SNNs, on the other hand, learn a similarity function. Thus, we can train it to see if the two images are the same (which we will do here). This enables us to classify new classes of data without training the network again.
  • 5. Pros and Cons of Siamese Networks: The main advantages of Siamese Networks are, ● More Robust to class Imbalance: a few images per class is sufficient for Siamese Networks to recognize those images in the future ● Nice to an ensemble with the best classifier: ● Learning from Semantic Similarity
  • 6. The drawback of the Siamese Networks are, ● Needs more training time than normal networks ● It is slower than normal classification type of learning ● It won’t output the probabilities of the prediction, but the distance from each class