The MNIST dataset of hand-written digits is ubiquitous in image classification, the de facto “hello world” of 2D CNNs. It’s also famously easy – a decent model can achieve 98% accuracy with a few minutes of training. To keep things interesting, let’s try dropping a dimension. Can we make MNIST fun again by treating it as a 1D signal classification problem? Images to Signals There are a lot of ways to convert a 2D image to a 1D signal.
