How to use Siamese network to handle sample-imbalanced data sets (with sample code)

The Siamese network is a neural network model used for metric learning, which is able to learn how to calculate a measure of similarity or difference between two inputs. Due to its flexibility, it is popular in numerous applications such as face recognition, semantic similarity calculation, and text matching. However, the Siamese network may face problems when dealing with imbalanced data sets, as it may overly focus on samples from a few classes and ignore the majority of samples. To solve this problem, several techniques can be used. One approach is to balance the data set through undersampling or oversampling. Undersampling means randomly removing some samples from the majority class so that they equal the number of samples from the minority class. Oversampling increases the number of samples in the minority class by copying or generating new samples so that it is equal to the number of samples in the majority class. This effectively balances the data set, but may lead to information loss or overfitting issues. Another method is to use weight adjustment. By assigning higher weights to minority class samples, the Siamese network's attention to the minority class can be increased. This improves model performance by focusing on a few classes without changing the dataset. In addition, some advanced metric learning algorithms can also be used to improve the performance of the Siamese network, such as generative adversarial networks (GAN) based on adversarial generative networks

1. Resampling technology

In an imbalanced data set, the number of category samples varies greatly. To balance the data set, resampling techniques can be used. Common ones include undersampling and oversampling to prevent excessive focus on a few categories.

Undersampling is to balance the sample size of the majority category and the minority category by deleting some samples of the majority category so that it has the same number of samples as the minority category. This approach can reduce the model's focus on the majority category, but may also lose some useful information.

Oversampling is to balance the sample imbalance problem by copying samples of the minority class so that the minority class and the majority class have the same number of samples. Although oversampling can increase the number of minority class samples, it may also lead to overfitting problems.

2. Sample Weight Technique

#Another way to deal with imbalanced data sets is to use the sample weight technique. This method can give different weights to samples of different categories to reflect their importance in the data set.

A common approach is to use class frequencies to calculate the weight of samples. Specifically, the weight of each sample can be set to $$

##w_i=\frac{1}{n_c\cdot n_i}

Where n_c is the number of samples in category c, n_i is the number of samples in the category to which sample i belongs. This method can balance the data set by giving higher weight to minority class samples.

3. Change the loss function

Siamese networks usually use contrastive loss functions to train models, such as triplet loss functions or cosine loss functions . When dealing with imbalanced data sets, an improved contrastive loss function can be used to make the model pay more attention to samples from the minority class.

A common approach is to use a weighted contrastive loss function, where samples from the minority class have higher weights. Specifically, the loss function can be changed to the following form:

L=\frac{1}{N}\sum_{i=1}^N w_i\cdot L_i

Where N is the number of samples, w_i is the weight of sample i, and L_i is the contrast loss of sample i.

4. Combine multiple methods

Finally, in order to deal with imbalanced data sets, multiple methods can be combined to train the Siamese network. For example, one can use resampling techniques and sample weighting techniques to balance the data set, and then use an improved contrastive loss function to train the model. This method can make full use of the advantages of various techniques and obtain better performance on imbalanced data sets.

For imbalanced datasets, a common solution is to use a weighted loss function, where less frequent classes are assigned higher weights. The following is a simple example showing how to implement a Siamese network with a weighted loss function in Keras to handle imbalanced data sets:

from keras.layers import Input, Conv2D, Lambda, Dense, Flatten, MaxPooling2D
from keras.models import Model
from keras import backend as K
import numpy as np

# 定义输入维度和卷积核大小
input_shape = (224, 224, 3)
kernel_size = 3

# 定义共享的卷积层
conv1 = Conv2D(64, kernel_size, activation='relu', padding='same')
pool1 = MaxPooling2D(pool_size=(2, 2))
conv2 = Conv2D(128, kernel_size, activation='relu', padding='same')
pool2 = MaxPooling2D(pool_size=(2, 2))
conv3 = Conv2D(256, kernel_size, activation='relu', padding='same')
pool3 = MaxPooling2D(pool_size=(2, 2))
conv4 = Conv2D(512, kernel_size, activation='relu', padding='same')
flatten = Flatten()

# 定义共享的全连接层
dense1 = Dense(512, activation='relu')
dense2 = Dense(512, activation='relu')

# 定义距离度量层
def euclidean_distance(vects):
    x, y = vects
    sum_square = K.sum(K.square(x - y), axis=1, keepdims=True)
    return K.sqrt(K.maximum(sum_square, K.epsilon()))

# 定义Siamese网络
input_a = Input(shape=input_shape)
input_b = Input(shape=input_shape)

processed_a = conv1(input_a)
processed_a = pool1(processed_a)
processed_a = conv2(processed_a)
processed_a = pool2(processed_a)
processed_a = conv3(processed_a)
processed_a = pool3(processed_a)
processed_a = conv4(processed_a)
processed_a = flatten(processed_a)
processed_a = dense1(processed_a)
processed_a = dense2(processed_a)

processed_b = conv1(input_b)
processed_b = pool1(processed_b)
processed_b = conv2(processed_b)
processed_b = pool2(processed_b)
processed_b = conv3(processed_b)
processed_b = pool3(processed_b)
processed_b = conv4(processed_b)
processed_b = flatten(processed_b)
processed_b = dense1(processed_b)
processed_b = dense2(processed_b)

distance = Lambda(euclidean_distance)([processed_a, processed_b])

model = Model([input_a, input_b], distance)

# 定义加权损失函数
def weighted_binary_crossentropy(y_true, y_pred):
    class1_weight = K.variable(1.0)
    class2_weight = K.variable(1.0)
    class1_mask = K.cast(K.equal(y_true, 0), 'float32')
    class2_mask = K.cast(K.equal(y_true, 1), 'float32')
    class1_loss = class1_weight * K.binary_crossentropy(y_true, y_pred) * class1_mask
    class2_loss = class2_weight * K.binary_crossentropy(y_true, y_pred) * class2_mask
    return K.mean(class1_loss + class2_loss)

# 编译模型，使用加权损失函数和Adam优化器
model.compile(loss=weighted_binary_crossentropy, optimizer='adam')

# 训练模型
model.fit([X_train[:, 0], X_train[:, 1]], y_train, batch_size=32, epochs=10, validation_data=([X_val[:, 0], X_val[:, 1]], y_val))

Among them, the weighted_binary_crossentropy function defines the weighted loss function, class1_weight and class2_weight respectively is the weight of category 1 and category 2, class1_mask and class2_mask are the masks used to shield category 1 and category 2. When training a model, you need to pass training data and validation data to the two inputs of the model, and pass the target variable as the third parameter to the fit method. Please note that this is just an example and is not guaranteed to completely solve the problem of imbalanced data sets. In practical applications, it may be necessary to try different solutions and adjust them according to the specific situation.

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