  Dimension of the encoder output (i.e., the code layer) in an autoencoder is a
key hyper-parameter for representing the input data in a proper space. This
dimension must be carefully selected in order to guarantee the desired
reconstruction accuracy. Although overcomplete representation can address this
dimension issue, the computational complexity will increase with dimension.
Inspired by non-parametric methods, here, we propose a metalearning approach to
increase the number of basis vectors used in dynamic sparse coding on the fly.
An actor-critic algorithm is deployed to automatically choose an appropriate
dimension for feature vectors regarding the required level of accuracy. The
proposed method benefits from online dictionary learning and fast iterative
shrinkage-thresholding algorithm (FISTA) as the optimizer in the inference
phase. It aims at choosing the minimum number of bases for the overcomplete
representation regarding the reconstruction error threshold. This method allows
for online controlling of both the representation dimension and the
reconstruction error in a dynamic framework.
