What is a Siamese network? It is also commonly known as one or a few-shot learning. They are popular because less labelled data is required to train them. Siamese networks are often used to figure out how similar or different two inputs are. This is useful for tasks like image and speech recognition. They are also used for natural language processing and bioinformatics. This article discusses how they are used, their advantages and disadvantages, and provides instructions on how to implement them in PyTorch.
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What is a Siamese network?
In a Siamese network, two identical sub-networks are linked together at the output layer of the neural network. The architecture of these sub-networks is the same, and they are frequently trained on the same task using different input data. The Siamese network’s goal is to assess whether the input data is similar or dissimilar by comparing the results of the two sub-networks.
For example, this type of network is often used to determine if two images or signatures are from the same person, like when an image or signature verification is done.
Siamese networks are used for signature verification.
What are typical applications of Siamese networks?
Siamese networks are often used in a variety of applications, including:
- Signature verification: Siamese networks can be trained to compare two signatures and determine if they are from the same person.
- Face recognition: Siamese networks can compare a probe image to a set of gallery images and determine if the probe image matches any of the gallery images.
- Object tracking: Siamese networks can track an object in a video by comparing its appearance in one frame to its appearance in the next frame.
- Image retrieval: Siamese networks can find similar images by comparing an image to a database of images.
- One-shot learning: By comparing the test image to a set of reference images, Siamese networks can recognise new objects with very few training samples.
- Image-caption matching: Siamese network can match a given image with its corresponding caption.
- Medical imaging: Siamese networks can compare medical images and find minor differences that could indicate a disease or condition.
How can Siamese networks be used in NLP?
Natural language processing (NLP) tasks like finding similar sentences or documents and classifying text can be done with Siamese networks.
- Sentence/document similarity: A Siamese network compares two sentences or documents and determines their likeness in this task. The network is trained on a dataset of sentence pairs labelled as similar or dissimilar. The network can then compare new sentence pairs and determine their similarity.
- Text classification: In this task, a Siamese network is used to classify a text into different categories. The network is trained on a dataset of texts labelled with corresponding categories. The network can then classify new texts into the appropriate category.
- Text matching: In this task, a Siamese network matches a given text to a set of reference texts and retrieves the most similar text from the reference set.
- Text-to-text similarity: In this task, a Siamese network can be used to find the similarity between two texts. It can be used in many applications, like question-answering and dialogue systems.
- Text-to-image matching: A Siamese network can match a given text to an image in this task. It can be used in many applications, like image captioning and multimedia retrieval.
- Text-to-speech matching: In this task, a Siamese network can match a given text to a speech; it can be used in many applications like speech recognition and synthesis.
The Siamese network architecture is typically used in NLP tasks. It consists of two identical encoder networks that process the input sentences or documents, followed by a comparison layer that determines how similar the two encoded representations are.
Advantages of Siamese networks
Siamese networks have several advantages, including:
- Data efficiency: Siamese networks can be trained with very little data, making them useful in one-shot learning tasks where only a few examples of each class are available.
- Robustness: Siamese networks are less affected by changes in the data they receive, which makes them more resistant to noise and other problems.
- Good at learning similarity: Since the siamese networks are trained to learn the similarity between two inputs, they are good at finding the similarity in different types of information like images, text, speech, etc.
- Transferability: Siamese networks can be trained on one task and then fine-tuned on a similar task with very little data, making them useful for transfer learning.
- Handling imbalanced data: Siamese networks can take an imbalanced dataset by making the decision based on the similarity of the input and not on the class distribution.
- Generalisation: Siamese networks can generalise well to new examples because they learn the similarity metric and not specific classes.
- Handling unseen classes: Siamese networks can take unseen classes to compare new examples to known ones and determine similarities.
- Handling multimodal data: Siamese networks can take multimodal data because they can compare two different modalities and assess similarity.
Disadvantages of Siamese networks
Siamese networks also have some disadvantages, including:
- Computational complexity: Siamese networks can be computationally expensive because they typically require training two identical sub-networks.
- Limited interpretability: Siamese networks can be challenging to interpret because the decision is based on the similarity between two inputs rather than on the input itself.
- Not broadly applicable: Siamese networks are mainly used for similarity-based tasks and may not be suitable for jobs requiring different decisions.
- Limited scalability: Siamese networks can be limited in their ability to scale to large datasets or many classes.
- Limited to pairwise comparison: Siamese networks are limited to pairwise comparison. It may only be suitable for tasks that require comparing up to two inputs simultaneously.
- Limited to specific architectures: Siamese networks are restricted to particular architectures. It may not be suitable for tasks that require other types of architecture.
- Limited to specific similarity functions: Siamese networks are restricted to particular similarity functions, so they may not be suitable for tasks that require other types of similarity functions.
- Limited to specific types of data: Siamese networks are restricted to particular kinds of data, so they may not be suitable for tasks that require other types of data.
How to implement a Siamese network
Here is a general outline of how to implement a Siamese network:
- Define the architecture of the sub-networks: The first step is to define the architecture of the two identical sub-networks that make up the Siamese network. This typically includes specifying the number of layers, the types of layers (e.g., convolutional layers, fully connected layers), and the number of neurons in each layer.
- Prepare the data: The next step is to prepare the data that will be used to train the Siamese network. This typically involves splitting the data into training and testing sets and creating pairs of similar and dissimilar examples.
- Train the sub-networks: Once the architecture and data are defined, the sub-networks can be trained. This typically involves passing the input data through the sub-networks, calculating the loss between the output of the two sub-networks, and backpropagating the error to update the weights of the sub-networks.
- Compare the output of the sub-networks: Once the sub-networks are trained, they can be used to compare the output of the two sub-networks for a given input pair. This can be done by calculating the similarity between the output of the two sub-networks, such as using a cosine similarity or euclidean distance.
- Fine-tune the network: After the sub-networks are trained, the network can be fine-tuned by adjusting the learning rate, adding more layers, and changing the architecture as needed.
- Test the network: Finally, the Siamese network can be tested on the test data to evaluate its performance. This typically involves calculating the accuracy of the network on the test data and comparing it to the training data.
It’s important to mention that this is a general outline, and the implementation may vary depending on the task and dataset you are working on.
Additionally, you may use pre-trained models, fine-tuning them with the siamese architecture.
Siamese network PyTorch example
Here is an example of how to implement a Siamese network for natural language processing (NLP) in PyTorch:
import torch import torch.nn as nn class Encoder(nn.Module): def __init__(self, input_size, hidden_size, num_layers): super(Encoder, self).__init__() self.embedding = nn.Embedding(input_size, hidden_size) self.lstm = nn.LSTM(hidden_size, hidden_size, num_layers, batch_first=True) def forward(self, x): x = self.embedding(x) _, (h, c) = self.lstm(x) return h[-1] class SiameseNetwork(nn.Module): def __init__(self, input_size, hidden_size, num_layers): super(SiameseNetwork, self).__init__() self.encoder = Encoder(input_size, hidden_size, num_layers) def forward(self, x1, x2): h1 = self.encoder(x1) h2 = self.encoder(x2) return h1, h2 # create the Siamese network input_size = len(word2index) hidden_size = 300 num_layers = 1 net = SiameseNetwork(input_size, hidden_size, num_layers) # define the loss function and optimizer criterion = nn.CosineSimilarity() optimizer = torch.optim.Adam(net.parameters(), lr=0.001) # train the network for epoch in range(num_epochs): for i, (sent1, sent2, label) in enumerate(train_data): sent1 = torch.tensor(sent1, dtype=torch.long) sent2 = torch.tensor(sent2, dtype=torch.long) label = torch.tensor(label, dtype=torch.float) h1, h2 = net(sent1, sent2) loss = criterion(h1, h2) optimizer.zero_grad() loss.backward() optimizer.step()
In this example the
word2index refers to a dictionary that maps each word in the vocabulary to a unique integer index. It is used to convert a sentence represented as a list of words into a tensor of integers that can be used as input to the network.
word2index dictionary is typically created by preprocessing the text data and tokenizing it into words. It is a common practice to use pre-trained embeddings like GloVe or BERT to initialize the weights of the embedding layer.
The Encoder class is a simple LSTM-based encoder that takes a sentence as input, passes it through an embedding layer and an LSTM, and returns the final hidden state of the LSTM.
The SiameseNetwork class is the main Siamese network, which takes two sentences as input, encodes them separately with the encoder, and returns the two encoded representations.
The CosineSimilarity loss function is used to calculate the similarity between the two encoded representations.
Finally, the network is trained with the Adam optimiser. Note that this is a very simplified example.
Consider using pre-trained embeddings, different types of encoders, and other techniques to improve the network’s performance.
In conclusion, a Siamese network is a type of neural network architecture in which two identical sub-networks are connected at the output layer. The primary purpose of the Siamese network is to compare the output of the two sub-networks and determine whether the input data is similar or dissimilar.
Siamese networks are often used in various tasks such as image or signature verification, face recognition, object tracking, image retrieval, one-shot learning, sentence and document similarity, image-caption matching, and medical imaging.
Siamese networks have several advantages, including data efficiency, robustness, good learning similarity, transferability, handling imbalanced data, generalisation, taking unseen classes, and handling multimodal data.
However, Siamese networks also have some disadvantages, such as computational complexity, limited interpretability, limited similarity-based tasks, limited scalability, limited pairwise comparison, limited to specific architectures, specific similarity functions, and specific data types.
To implement a Siamese network, you need to define the architecture of the sub-networks, prepare the data, train the sub-networks, compare the output of the sub-networks, fine-tune the network and test the network.