You can expand your focus with the purpose of either decreasing costs or finding value. That is, either considering how you can share code/weights/ideas between two tasks you must do (build better internal representations in order to make solving future problems more efficient), or mining for value (see Set wide goal and Set deep goal).
In general, increase the amount of state you are attempting to attended to. This may be natural language in your notes, your open browser tabs, the weights in a net being trained, or the weights on the neurons in your brain. Imagine you could see your own weights in your brain. What new set of mental weights are you attending to with this new text editor or web browser tab?
Expanding your focus is not the same as “Alternating” or “Divided” attention in the model of Sohlberg and Mateer, or to Human multitasking. In general it’s not about switching tasks or controlling your attention, but merely about looking at the bigger picture.
Unorganized notes generally indicate you really want to focus on a topic, but believe there is value
in a thought you should save for later. You can add these (or any other thoughts) to the set of
notes you are attending to, without making them show up in
git as uncommitted changes, by adding
them to a file in your text editor but not saving the file (e.g.
vim). Another option is
to open a “reminder tab” in your web browser and move it out of the way.
To analogize to the work that nets do, you may be temporarily switching to task A (a different perspective) from task B in an effort to help you perform better on task B (in the long term). Of course, you expect to see better performance on task A in the short term.
Transfer learning occurs when subnets are trained on two different tasks in series (e.g. classification and object detection). When we do this kind of transfer learning we often throw away our ability to do the original task (classification) but there’s no reason that head needs to be thrown away. What if we went back to training for classification a few more times, even if object detection is our final goal? If the loss continues to go down, why not? We typically do not automate transfer learning techniques, even if we used them to train a model that is used in e.g. production (which is unfortunate).
In the language of focus, you can see this as going in and out of focus on different tasks. When the net is attempting to perform classification, it is attending to both the weights that are solely for classification and the shared weights used for both classification and detection. It could regularly expand its focus to all tasks it can perform, however, and then narrow back into the detection task (assuming it was last working on classification).
Expanding your focus is also sometimes similar to increasing your batch size in training. That is, you are sometimes only looking for a different perspective on the same problem in a new web article. Perhaps you started a web search looking for a higher-quality web article, but are satisfied to only find a similar quality resource that provides a second or third perspective. The upside is that two perspectives can help you learn; see Evaluate pedagogical tool for thoughts on multi-modal resources. The downside is that increasing your batch size may be an indicator that you are searching hopelessly for a resource that is already in your own words; the cost of a larger batch size is freezing part of your network. See also Continuous partial attention.
git grep for all the key words in question, and use or delete your old thoughts. This is the
equivalent of querying your own notes.