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This work introduces a novel activation unit that can be efficiently employedin deep neural nets (DNNs) and performs significantly better than thetraditional Rectified Linear Units (ReLU). The function developed is a twoparameter version of the specialized Richard's Curve and we call it AdaptiveRichard's Curve weighted Activation (ARiA). This function is non-monotonous,analogous to the newly introduced Swish, however allows a precise control overits non-monotonous convexity by varying the hyper-parameters. We firstdemonstrate the mathematical significance of the two parameter ARiA followed byits application to benchmark problems such as MNIST, CIFAR-10 and CIFAR-100,where we compare the performance with ReLU and Swish units. Our resultsillustrate a significantly superior performance on all these datasets, makingARiA a potential replacement for ReLU and other activations in DNNs.

ARiA: Utilizing Richard's Curve for Controlling the Non-monotonicity of the Activation Function in Deep Neural Nets