Systems and methods for out-of-distribution classification

A system for training a neural network for out-of-distribution detection, the system comprising:

• a communication interface that receives a plurality of training samples having a first feature dimension;
• a memory containing machine readable medium storing machine executable code;
• and
• one or more processors coupled to the memory and configurable to execute the machine executable code to cause the one or more processors to:
• train the neural network using the plurality of training samples;
• generate, via the neural network, a classification output in response to an input sample having the first feature dimension;
• generate a pruned output by using a Gaussian distribution based sparsification vector to reduce a dimension of the classification output to a second feature dimension;
• computing a loss based on the pruned output and the input sample;
• and
• updating the sparsification vector by backpropagation based on the computed loss.