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As an experiment, I'd like to train a model to label a few rows of data on a 2D tensor. F.e. on a black and white image, label the "darkest" and "lightest" row. Is this a task for CNN or some simpler methods would do?
Does this task fall into one of existing categories?
Is the number of rows fixed or variable? Can the row predictions be made independently or do they require information from other rows?
If predictions are independent, you could take your input image of size (rows, cols, channels), reshape it to (rows, cols*channels) and run it through a MLP that predicts a value for each row separately. This would work for fixed or variable number of rows
If number of rows is fixed and predictions are dependent, you could use a pretrained CNN to map the input image to a fixed length embedding vector. Then send the embedding vector to a MLP that outputs a fixed number of predictions (fixed at number of rows).
If rows are variable and predictions are dependent, you could do the same pretrained CNN -> embedding, but decode with an autoregressive model that can predict variable length outputs for each input.
This sounds like a simple matrix operation:
