The visualizations of features captured by filters in neural networks like VGG-19 and InceptionNet are typically created through a process called "feature visualization" or "activation maximization." The goal is to generate an input image that maximizes the activation of a specific filter or neuron in a chosen layer of the neural network. These visualizations help us understand what kind of patterns, textures, or concepts a particular filter is responsive to. Here's a general overview of how these visualizations are created:
Selecting a Layer and Filter: Choose the layer and filter (or neuron) you want to visualize. Layers deeper in the network tend to capture more abstract and complex features.
Initialization: Start with a random or noise image as the initial input. This serves as the starting point for optimization.
Optimization Objective: Define an objective function to optimize the input image. The objective is to maximize the activation of the chosen filter in the selected layer. In mathematical terms, you want to maximize the value of the activation tensor at that filter's position.
Backpropagation: Use gradient descent or a similar optimization algorithm to iteratively update the input image to maximize the activation. Compute the gradient of the activation with respect to the input image and adjust the image in the direction that increases the activation.
Regularization: To prevent the input image from becoming noisy or unrealistic, apply regularization techniques. Common regularization methods include L2 regularization (to encourage smoother images) and total variation regularization (to reduce noise).
Iterations: Repeat the optimization process for a certain number of iterations or until a stopping criterion is met. During each iteration, the input image is gradually adjusted to activate the chosen filter more strongly.
Visualization: After optimization, the final input image represents the features that maximize the activation of the selected filter. This image is often referred to as the "feature visualization" or "maximally activating image."
Post-processing: The generated image may be post-processed to enhance its visual quality or clarity, such as adjusting its brightness and contrast.
Analysis: Examine the resulting image to gain insights into what kind of patterns or concepts the chosen filter is responsive to. This can provide valuable information about the network's feature representation.
Repeat for Other Filters: You can repeat this process for different filters or neurons in the same layer to visualize what each one captures.
It's important to note that feature visualization is an interpretive tool and doesn't always produce realistic or meaningful images. The generated visualizations help researchers and practitioners understand the learned representations within neural networks, but they may not directly reflect how the network processes real-world data. Additionally, the success of feature visualization can vary depending on the architecture and complexity of the neural network.