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

arXiv 2308.05724 ↗First posted 10 Aug 2023cs.LG cs.CE cs.NEcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 98 references that are in the map. First posted on arXiv 10 Aug 2023.

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16 of 98 references.

References · 16

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  11. 2014Learning Activation Functions to Improve Deep Neural Networkscited by 63
  12. 2018Collapse of Deep and Narrow Neural Netscited by 37
4 more
  1. 2018PLU: The Piecewise Linear Unit Activation Functioncited by 11
  2. 2014Enhanced Higgs to τ^+τ^- Searches with Deep Learningcited by 5
  3. 2020Human Action Recognition and Assessment via Deep Neural Network Self-Organizationcited by 1
  4. 2023Optimal Input Gain: All You Need to Supercharge a Feed-Forward Neural Networkcited by 1

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