Introducing the DOME Activation Functions
This presents a novel activation function for neural networks that could improve robustness in classification tasks.
The authors introduced DOME activation functions that induce class-compactness and regularization in neural network embeddings, demonstrating they provide extra robustness against adversarial attacks.
In this paper, we introduce a novel non-linear activation function that spontaneously induces class-compactness and regularization in the embedding space of neural networks. The function is dubbed DOME for Difference Of Mirrored Exponential terms. The basic form of the function can replace the sigmoid or the hyperbolic tangent functions as an output activation function for binary classification problems. The function can also be extended to the case of multi-class classification, and used as an alternative to the standard softmax function. It can also be further generalized to take more flexible shapes suitable for intermediate layers of a network. We empirically demonstrate the properties of the function. We also show that models using the function exhibit extra robustness against adversarial attacks.