Deep Neural Networks (DNNs) are ubiquitous in modern machine learning tasks like image and speech recognition. They take in input data and make decisions based on that input. The activation function used in the DNNs is an essential component that determines the output. In this context, a new activation unit has been introduced called Adaptive Richard's Curve weighted Activation (ARiA). The following discussion is an overview of ARiA and its significance over traditional Rectified Linear Units (ReLU).
What is ARiA?
ARiA is a new activation function, and it has two hyperparameters that allow non-monotonous, convex output to control the derivatives better. This flexibility allows fine-tuning of the activation function to obtain optimal performance for specific datasets. As a result, ARiA could replace other activation functions, namely ReLU and Swish, which have been used extensively in DNNs.
ARiA vs ReLU
ReLU is a popular activation function used in neural networks that has a relatively simple mathematical equation. It is given by f(x) = max(0, x), where x is the output of the previous layer. In essence, ReLU activates a neuron if the input is positive but acts as an off switch if the input is negative.
ARiA is different from ReLU because it has a more complicated equation, but it also helps to solve some of the problems that arise when using ReLU. One of the significant issues with ReLU is that it is prone to the "dying ReLU" problem when the output is close to or equal to zero. This problem occurs because the gradient is zero when the output is negative, and therefore, the neuron does not update its weights, known as "dead neurons". Such neurons prevent the network from learning effectively.
ARiA vs Swish
Swish is another activation function that had been introduced recently that has outperformed ReLU in certain tasks. Swish is obtained by applying the sigmoid function to the output of the previous layer multiplied by the input. However, Swish has some limitations such as the difficulty to tune the curvature of the function.
ARiA performs similarly to Swish, but with a significant advantage that it is less computationally intensive. Additionally, with the two hyperparameters, users can control the non-monotonous convexity of the function more precisely, allowing for better performance on complex datasets.
Applications of ARiA
To test the efficiency of ARiA, it has been applied to image recognition tasks using benchmark datasets such as MNIST, CIFAR-10, and CIFAR-100. Results show that ARiA significantly outperforms ReLU and Swish in terms of accuracy and convergence speed. This suggests that ARiA has the potential to become a new standard activation function for DNNs, and it can significantly improve performance in various machine learning applications.
ARiA is a new activation function, which can be efficiently used in deep neural networks. It is a non-monotonous function, and it has two hyperparameters that allow precise control over its non-monotonous convexity. ARiA is superior to traditional functions like ReLU and Swish, as it has been demonstrated by its usage in benchmarks like MNIST, CIFAR-10, and CIFAR-100. Overall, ARiA has the potential to become the new standard activation function, enabling better performance in various machine learning applications.
