A Significantly Better Class of Activation Functions Than ReLU Like Activation Functions
<|reference_start|>A Significantly Better Class of Activation Functions Than ReLU Like Activation Functions: This paper introduces a significantly better class of activation functions than the almost universally used ReLU like and Sigmoidal class of activation functions. Two new activation functions referred to as the Cone and Parabolic-Cone that differ drastically from popular activation functions and significantly outperform these on the CIFAR-10 and Imagenette benchmmarks are proposed. The cone activation functions are positive only on a finite interval and are strictly negative except at the end-points of the interval, where they become zero. Thus the set of inputs that produce a positive output for a neuron with cone activation functions is a hyperstrip and not a half-space as is the usual case. Since a hyper strip is the region between two parallel hyper-planes, it allows neurons to more finely divide the input feature space into positive and negative classes than with infinitely wide half-spaces. In particular the XOR function can be learn by a single neuron with cone-like activation functions. Both the cone and parabolic-cone activation functions are shown to achieve higher accuracies with significantly fewer neurons on benchmarks. The results presented in this paper indicate that many nonlinear real-world datasets may be separated with fewer hyperstrips than half-spaces. The Cone and Parabolic-Cone activation functions have larger derivatives than ReLU and are shown to significantly speedup training.<|reference_end|>
@article{noel2024a,
    title={A Significantly Better Class of Activation Functions Than ReLU Like
  Activation Functions},
    author={Mathew Mithra Noel, Yug Oswal},
    journal={arXiv preprint arXiv:2405.04459},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.04459},
    primaryClass={cs.AI cs.CV cs.LG cs.NE}
}