4 ms·
Great, understood a lot more than I had from the DeepMinds paper alone. Thought the mathematics was slightly beyond me, I got the gist of it still. Although thi
by itchyjunk 10y ago
Great, understood a lot more than I had from the DeepMinds paper alone. Thought the mathematics was slightly beyond me, I got the gist of it still.
Although this was talking about reinforcement learning in Atari, I was wondering if its works for other domain as well? supervised, unsupervised etc. If it does and say you have sparse data for task B but rich data for task A. Is this saying training first on A than transfer learning B makes it perform better on B? (As I type it, it's sounding like semi-supervised but it's not what I am trying to ask. :P)
P.S: Pictures helped.
- fnbr 10y agoIt 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.