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The classifier was likely a convolutional network, so the assumption of the image being a 2D grid was baked into the architecture itself - it didn't have to be
by thegeomaster 2y ago
The classifier was likely a convolutional network, so the assumption of the image being a 2D grid was baked into the architecture itself - it didn't have to be represented via the shape of the input for the network to use it.
- torginus 2y agoI don't think so - convolutional neural networks also operate over 1D flat vectors - the spatial relationship of pixels is only learned from the training data.
- thegeomaster 2y agoThis is not true. CNNs perform 2D convolution, conceptually "sliding" a 2 dimensional kernel with learnable weights over the input image across two dimensions. Perhaps it wasn't a convolutional network after all, but a simple fully-connected feed-forward network taking all pixels as input? Could be viable for a toy example (MNIST).