If you've been beat to the press, you might as well release your paper to salvage something from your sunk costs of time/effort/GPUs. It's inefficient because in solving almost the same exact problems, they are duplicating each other's work instead of sharing the intermediate steps. If there were more sharing of low-grade information, along the lines of 'I'm working on a lipreading CNN, it's going pretty well' 'oh hey we're working a lipreading CNN too!', then the datasets and GPU clusters and math could be pooled and better single results released quicker. As it is, now you have to read two different papers about lipreading CNNs and puzzle over the differences and two different papers about the concrete distribution trick, and they probably all came out a month or two later because everyone had to redo work for their separate system & paper.
Duplicated work is not necessary a sign of inefficiency, though.
My hope would be that they each learned something slightly different in solving the same problem. Eventually, things may converge to a single answer. However, there is no evidence to see that we should demand the convergence at the beginning.
So, the shame here is if folks are not comparing and contrasting the different solutions to the same problem. I confess I am guilty in that I have not read both papers. But I will try to see if it can help me understand.
