For updating its own weights, there is the idea of fast weights (https://arxiv.org/abs/1610.06258 https://arxiv.org/abs/1610.06258). This idea of updating its

For updating its own weights, there is the idea of fast weights (https://arxiv.org/abs/1610.06258 https://arxiv.org/abs/1610.06258). This idea of updating its own weights is not new. Schmidhuber has done some work on this (http://people.idsia.ch/~juergen/deep-learning-miraculous-year-1990-1991.html http://people.idsia.ch/~juergen/deep-learning-miraculous-yea...). The main idea is also that the model can modify even itself (so not having two separate nets) (see Schmidhuber, "Steps towards `self-referential' learning"). Online learning / continual learning is yet another (orthogonal) topic. This is the setting where new (training) data becomes available all the time, and the model should use that data, i.e. all input (from inference) is used to update and train the model further. This can be done by standard backpropagation. The problem is usually to overcome catastrophic forgetting in this case. See for example: https://deepmind.com/blog/article/enabling-continual-learning-in-neural-networks https://deepmind.com/blog/article/enabling-continual-learnin...