Evaluating Model Performance with Hard-Swish Activation Function Adjustments
<|reference_start|>Evaluating Model Performance with Hard-Swish Activation Function Adjustments: In the field of pattern recognition, achieving high accuracy is essential. While training a model to recognize different complex images, it is vital to fine-tune the model to achieve the highest accuracy possible. One strategy for fine-tuning a model involves changing its activation function. Most pre-trained models use ReLU as their default activation function, but switching to a different activation function like Hard-Swish could be beneficial. This study evaluates the performance of models using ReLU, Swish and Hard-Swish activation functions across diverse image datasets. Our results show a 2.06% increase in accuracy for models on the CIFAR-10 dataset and a 0.30% increase in accuracy for models on the ATLAS dataset. Modifying the activation functions in architecture of pre-trained models lead to improved overall accuracy.<|reference_end|>
@article{pydimarry2024evaluating,
    title={Evaluating Model Performance with Hard-Swish Activation Function
  Adjustments},
    author={Sai Abhinav Pydimarry, Shekhar Madhav Khairnar, Sofia Garces Palacios,
  Ganesh Sankaranarayanan, Darian Hoagland, Dmitry Nepomnayshy, Huu Phong
  Nguyen},
    journal={RECPAD 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.06879},
    primaryClass={cs.CV}
}