Basically, what this means is that we’re using a neural network to search for the best architecture (i.e., structure) for recognizing objects in images. But here’s where it gets interesting: instead of just searching through all possible architectures and picking the one with the highest accuracy, we’re also taking into account how often each architecture is used during training. This means that if an architecture only shows up occasionally (i.e., intermittently), but still performs well when it does appear, it might be worth keeping around even if its overall performance isn’t as high as some of the other architectures.

To illustrate this with a silly example: let’s say we have two neural networks that both recognize cats and dogs equally well (i.e., they have an accuracy of 95% for each class). However, one network only appears during training when there are cats in the image, while the other network is used more frequently but has a slightly lower overall accuracy (say, 90% for cats and 85% for dogs). If we’re just looking at raw performance numbers, it might seem like the second network is better because its average accuracy is higher. But if we take into account how often each network is used during training, we might realize that the first network (which only appears when there are cats in the image) could actually be more useful for recognizing cats specifically, even though its overall performance numbers aren’t as high.

So basically, by using an “intermittent-aware” approach to neural architecture search, we can find architectures that perform well on specific tasks (like recognizing cats or dogs), even if they don’t have the highest overall accuracy across all possible inputs. This could be really useful for applications like medical imaging or autonomous driving, where it might not matter as much whether a model is accurate in general, but rather whether it can accurately identify certain types of objects (like tumors or pedestrians) when they appear in specific contexts.

Hope that helps clarify things!