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In past few years, linear rectified unit activation functions have shown itssignificance in the neural networks, surpassing the performance of sigmoidactivations. RELU (Nair & Hinton, 2010), ELU (Clevert et al., 2015), PRELU (Heet al., 2015), LRELU (Maas et al., 2013), SRELU (Jin et al., 2016),ThresholdedRELU, all these linear rectified activation functions have its ownsignificance over others in some aspect. Most of the time these activationfunctions suffer from bias shift problem due to non-zero output mean, and highweight update problem in deep complex networks due to unit gradient, whichresults in slower training, and high variance in model prediction respectively.In this paper, we propose, "Thresholded exponential rectified linear unit"(TERELU) activation function that works better in alleviating in overfitting:large weight update problem. Along with alleviating overfitting problem, thismethod also gives good amount of non-linearity as compared to other linearrectifiers. We will show better performance on the various datasets usingneural networks, considering TERELU activation method compared to otheractivations.

Overcoming Overfitting and Large Weight Update Problem in Linear Rectifiers: Thresholded Exponential Rectified Linear Units