Topic: example-recognizing-images
In the video, you show what the neurons are “detecting”. How do you know this? Is this just a rendering of the values of the input that maximize the output (activation) for the layer’s neuron for each neuron? What do the boxes actually represent?
Can you give a time mark where your question applies?
2:38
Hi Kelly, welcome to the community!
In this context, Prof. Andrew Ng shows what features each neuron detects at different stages of the network, and illustrates how the network transforms raw image pixels into abstract features (such as edges, shapes, or more complex patterns). Each neuron in a layer recognizes specific patterns or features in the input image. For example, early layers may recognize simple edges or textures, while deeper layers may recognize more complex features such as parts of objects.
To understand what a neuron is detecting, one can generate images that maximize the activation of a particular neuron. In practice, this is done by starting with a random input image and iteratively modifying it (using gradient ascent) to increase the activation of the neuron. These visualizations show the input patterns (such as textures or shapes) that activate the neuron the most, helping to interpret what the neuron has “learned”. This method helps to interpret what neurons in a neural network are “looking for”. Specifically, one can visualize the type of input that maximizes the output for a given neuron.
The visualizations represent the input values (such as pixel intensities) that result in the highest activation for a given neuron. The visualizations are often generated by optimization techniques that modify the input image to maximize the neuron’s response. These visualizations aren’t literal images of the training data but rather show idealized patterns that represent what the neuron is most sensitive to. For example, a neuron might activate strongly when it detects edges, curves, or other features. So the rendered image maximizes the response of that neuron to show what it is “looking for” in the input data.
The boxes shown correspond to the specific part of the image (receptive field) to which a neuron is sensitive. Each box represents a small region of the input image in a layer that the neuron is analyzing. These boxes show the regions where the neurons focus their attention and extract information such as features.
Hope this helps!
Thanks @nadtriana for the explanation, it was very helpful. In your last paragraph you say:
The boxes shown correspond to the specific part…
Does that mean this network in the example is a convolutional network and that each of the neurons is not “fully connected” to the input layer, so the image is just showing the part it is connected to, or do you mean some detection was done to filter the input layer to only the “interesting” part to the neuron (for a fully connected dense layer)?
In the example, the network shown is a convolutional neural network (CNN), in which the neurons in each layer are not fully connected to the entire input. Instead, each neuron processes only a small portion of the image (its receptive field), so the boxes highlight the regions to which specific neurons are sensitive. This allows the network to capture spatial hierarchies of features, from simple to complex.