What is activation maximization in AI?

What is activation maximization in AI?

Activation maximization is a technique used in neural networks to visualize what a model has learned by maximizing the activation of specific neurons.

How does it help in understanding neural networks?

It helps by revealing the features or patterns that activate certain neurons, providing insights into the model's decision-making process.

Can you give an example of how it's applied?

Sure! For instance, in image classification, activation maximization can generate images that strongly activate a specific class, helping us understand what the model associates with that class.

Are there any limitations to this technique?

Yes, one limitation is that it may not always produce interpretable results, as the generated images can sometimes be abstract or not resemble real-world objects.