Are there any downsides of using a fixed seed for a neural network's weight initialization? For example, if we set the random seed to be 0, will we run into any problems? For example, maybe for seed 0, we can only reach a certain training error, but other seeds will converge to a much lower error
I'm specifically concerned about supervised learning on point cloud data, but curious about whether it matters in general whenever you use a neural network.
When you use a particular seed, it actually ceases to become a random initialization and is instead fixed. I believe the only reason to actually do this would be for reliable reproduction in research and not as a method of training production models.