Symmetric block-low-rank layers for fully reversible multilevel neural networks

Understand

Factors that limit the size of the input and output of a neural network include memory requirements for the network states/activations to compute gradients, as well as memory for the convolutional kernels or other weights.

• The memory restriction is especially limiting for applications where we want to learn how to map volumetric data to the desired output, such as video-to-video.
• Recently developed fully reversible neural networks enable gradient computations using storage of the network states for a couple of layers only.
• While this saves a tremendous amount of memory, it is the convolutional kernels that take up most memory if fully reversible networks contain multiple invertible pooling/coarsening layers.