Shaping a neural network architecture utilizing learnable sampling layers

A system comprising:

• at least one memory device comprising a neural network comprising a plurality of sampling layers and a plurality of network weights;
• and
• at least one server device configured to cause the system to:
• provide a neural network shaping mechanism for at least one sampling layer of the neural network, the neural network shaping mechanism comprising a learnable scaling factor between a sampling rate of the at least one sampling layer and an additional sampling function;
• generate output feature maps based on input to the at least one sampling layer using the sampling rate of the at least one sampling layer and the additional sampling function;
• learn the scaling factor based on an interpolation between the output feature maps while jointly learning the plurality of network weights;
• and
• modify the sampling rate of the at least one sampling layer based on the learned scaling factor.

A computer-implemented method for morphing neural network architectures comprising:

• initializing a neural network comprising a plurality of sampling layers and a plurality of network weights;
• providing one or more neural network shaping mechanisms between sampling layers of the neural network;
• performing a step for learning scaling factors for the one or more neural network shaping mechanisms;
• and
• shaping an architecture of the neural network based on the learned scaling factors.