While brainstorming on a new project, I stumbled upon a problem. I would like to create a neural network to encode a sequence of objects and get the gist of the sequence. However, the sequence is of variable-length and order-insensitive, that is, the order of the objects should not matter to the output.
The variable-length part can be solved using padding, but the order-insensitive property troubles me. I've looked at LSTM, GRU and Transformer networks but they are seq2seq encoders and are not order-insensitive.
My search on the topic led to nothing. Do you know of any solution for this use-case ?