Submitted by give_me_the_truth t3_yni223 in MachineLearning
give_me_the_truth OP t1_iv9kyup wrote
Reply to comment by fnbr in [D] Enforcing object order in object detection by give_me_the_truth
It is like I know beforehand that object1 will always be on the left of object2. If network predicts object2 to left of object1 then I have to discard those predictions and I know it is incorrect. If I incorporate this constraint then I can get predictions that I am looking for.
cmilkau t1_ivba811 wrote
History shows that it is usually better to keep learning as simple as possible and not force the NN to learn the way you think it would be best. If you want the NN to recognize two objects, better don't waste time training it to check their spatial relationship, or something else that isn't what you're looking for (even though related).
Just check whether object 1 is reported as object 1 and object 2 is reported as object 2. This can be two separate losses you add. Don't worry about whether the actually reported objects are left or right from each other, just check whether they are in the right place each for themselves.
give_me_the_truth OP t1_ivdfyec wrote
>History shows that it is usually better to keep learning as simple as possible and not force the NN to learn the way you think it would be best.
Got it. Any references you have off the top of your head?
cmilkau t1_ivdzr4p wrote
Better, IIRC there's an overview paper. I'll try to find it
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