UTILIZATION OF AUTO ENCODERS AS A GENERALIZATION METHOD FOR ANTI-SPOOFING CLASSIFICATION
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Abstract
When training large neural networks, explicit generalisation procedures are required to
ensure that the model's predictions can be generalised. Auto-encoders now have a new
generalisation result. As a result of the data's limited diversity, generalisation issues arise
when developing models aimed at tackling these types of problems. Regularization is often
added by including a regularisation parameter in the objective function; however, in this
paper, we will provide an alternative regularisation approach that is equal to the one
commonly used. The fact that other regularisation strategies failed to eliminate overfitting
implies that these regularisation techniques may be used as a pre-processing step for other
algorithms.
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