It would probably depend on how closely related the tasks are. For instance, there's been a few papers talking about "transfer learning" where a network is tra

It would probably depend on how closely related the tasks are. For instance, there's been a few papers talking about "transfer learning" where a network is trained on video data for the equivalent of tens of thousands of hours, and then used to control a robot (where the robot's inputs are partly from video). The pre-lesrned weights help significantly, as you'd imagine. In another sense, it's often useful to use a pretrained network as the input to your model (so you'd run the images through another network that outputs a simplified representation of the images, and then run a second model on that). That's currently quite useful; I could see something like that being super useful here. Train on one task with lots of data, and then switch to something similar with less data.