Attention mechanism - Recurrent Attention Model (RAM)

Recurrent Attention Model (RAM) is a neural network-based attention model for processing images of variable size and orientation. RAM is designed to mimic the attention mechanism of the human visual system, which focuses the eye on different parts of an image at different points in time for more in-depth processing.

The basic idea of ​​RAM is to select a region of interest at each time step and use a local feature map to process this region. To achieve this, RAM introduces a learnable "attention module" that selects a region of interest at each time step and generates a feature map associated with that region.

Specifically, RAM consists of two main parts: a differentiable attention module and an RNN-based classifier. The attention module is responsible for selecting regions of interest and generating local feature maps, while the RNN classifier uses these local feature maps to predict the category of the image.

In RAM, the attention module consists of two sub-modules: the location network and the receptive field network. The location network is a fully connected layer responsible for predicting the location of interest at the next time step. The receptive field network is a convolutional neural network used to generate local feature maps. It takes the whole image as input and then generates local feature maps at locations chosen by the attention module. Finally, these local feature maps are concatenated together and passed to an RNN-based classifier for classification.

Sample code to implement Recurrent Attention Model using PyTorch:

class RAM(nn.Module):
    def __init__(self, input_size, hidden_size, num_classes, n_steps):
        super(RAM, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.num_classes = num_classes
        self.n_steps = n_steps
        
        # Attention Network
        self.attention = GaussianAttention(input_size, hidden_size)
        
        # Classification Network
        self.classifier = nn.Sequential(
            nn.Linear(hidden_size * 16 * 16, 128),
            nn.ReLU(),
            nn.Linear(128, num_classes)
        )
        
    def forward(self, x):
        batch_size = x.size(0)
        x = x.view(batch_size, self.input_size, -1)
        
        # run attention network for n_steps
        x, mu, sigma = self.attention(x, self.n_steps)
        
        # flatten input for classification network
        x = x.view(batch_size, -1)
        
        # classify using classification network
        out = self.classifier(x)
        
        return out, mu, sigma

In the code above, we defined a model class called RAM. This model class includes Attention Network and Classification Network. In the forward method, we first reshape the input image into a 2D tensor and pass it to the Attention Network. Then, we flatten the output of Attention Network and pass it to Classification Network for classification.

We also return the output mu and sigma of the Attention Network to use the reconstruction loss function during training. The purpose of the reconstruction loss function is to reconstruct the original image into the input image through the generated gaze points, and calculate the difference between the generated image and the original image using mu and sigma.

During training, we can use the backpropagation algorithm (backpropagation) to update the weights of the neural network. The backpropagation algorithm is an optimization algorithm based on gradient descent, which adjusts the network weights by calculating the gradient of the loss function to each weight, thereby minimizing the loss function.

In the backpropagation algorithm, we feed the input of training data into the neural network and then calculate the loss function based on the output of the network. Next, we compute the gradient of the loss function with respect to each weight and update the weights using gradient descent. This process is repeated until the loss function converges to a minimum value.

The backpropagation algorithm is a very common neural network training algorithm that can be used to train various types of neural networks, including fully connected neural networks, convolutional neural networks, and recurrent neural networks.