This is somewhat practical for neural networks. For example, instead of minimizing the loss function, why not tweak the input to maximize a neuron’s activation?

This is somewhat practical for neural networks. For example, instead of minimizing the loss function, why not tweak the input to maximize a neuron’s activation? Or with a CNN, maximize the sum of a kernel’s channel? This would tell us what the neuron corresponds with. This is what Google did with DeepDream. An explanation/tutorial, with clean images of the process: https://github.com/tensorflow/tensorflow/blob/r0.10/tensorflow/examples/tutorials/deepdream/deepdream.ipynb https://github.com/tensorflow/tensorflow/blob/r0.10/tensorfl... Google’s investigation of it’s GoogLeNet architecture: http://storage.googleapis.com/deepdream/visualz/tensorflow_inception/index.html http://storage.googleapis.com/deepdream/visualz/tensorflow_i... Now, I say somewhat because results can be visually confusing, ex Google’s analysis. Even then, we can see the progression of layer complexity as we go deeper into ImageNet. Plus, we can see mixed4b_5x5_bottleneck_pre_relu has kernels that seem to correspond with noses and eyes. mixed_4d_5x5_pre_relu has a kernel that seems to correspond with cat faces.