Optimizing Performance of Feedforward and Convolutional Neural Networks through Dynamic Activation Functions

• URL: http://arxiv.org/abs/2308.05724v2
• Date: Sun, 18 Feb 2024 21:53:54 GMT
• Title: Optimizing Performance of Feedforward and Convolutional Neural Networks through Dynamic Activation Functions
• Authors: Chinmay Rane, Kanishka Tyagi, Michael Manry
• Abstract summary: Deep learning training algorithms are a huge success in recent years in many fields including speech, text,image video etc. Deep and deeper layers are proposed with huge success with resnet structures having around 152 layers. Shallow convolution neural networks(CNN's) are still an active research, where some phenomena are still unexplained.
• Score: 0.46040036610482665
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning training training algorithms are a huge success in recent years in many fields including speech, text,image video etc. Deeper and deeper layers are proposed with huge success with resnet structures having around 152 layers. Shallow convolution neural networks(CNN's) are still an active research, where some phenomena are still unexplained. Activation functions used in the network are of utmost importance, as they provide non linearity to the networks. Relu's are the most commonly used activation function.We show a complex piece-wise linear(PWL) activation in the hidden layer. We show that these PWL activations work much better than relu activations in our networks for convolution neural networks and multilayer perceptrons. Result comparison in PyTorch for shallow and deep CNNs are given to further strengthen our case.

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