APTx: better activation function than MISH, SWISH, and ReLU's variants used in deep learning. (arXiv:2209.06119v3 [cs.LG] UPDATED)

Activation Functions introduce non-linearity in the deep neural networks.
This nonlinearity helps the neural networks learn faster and efficiently from
the dataset. In deep learning, many activation functions are developed and used
based on the type of problem statement. ReLU's variants, SWISH, and MISH are
goto activation functions. MISH function is considered having similar or even
better performance than SWISH, and much better than ReLU. In this paper, we
propose an activation function named APTx which behaves similar to MISH, …

arxiv deep learning function relu swish variants