3 ms·
I wonder why that is? because they are trained with dropout?
by WanderPanda 2y ago
I wonder why that is? because they are trained with dropout?
- david-gpu 2y agoProbably because of how bloody large they are. The quantization errors likely cancel each other out over the sum of so many terms. Same reason why you can get a pretty good reconstruction when you add random noise to an image and then apply a binary threshold function to it. The more pixels there are, the more recognizable will be the B&W reconstruction.