Activated Gradients for Deep Neural Networks.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Deep Learning

Background:

• Deep neural networks (DNNs) face challenges like ill-conditioning, vanishing/exploding gradients, and saddle points, hindering performance and training.
• These issues often lead to poor model accuracy and training instability.

Purpose of the Study:

• To introduce a novel Gradient Activation Function (GAF) designed to mitigate common training problems in deep neural networks.
• To theoretically and empirically validate the effectiveness of GAF in improving DNN training and performance.

Main Methods:

• A new Gradient Activation Function (GAF) is proposed, which modifies gradients by enlarging small ones and restricting large ones.
• Theoretical analysis is provided to establish conditions for GAF and prove its ability to alleviate training problems.
• Stochastic Gradient Descent (SGD) with GAF is analyzed for convergence rate improvements.

Main Results:

• The GAF effectively enlarges small gradients and restricts large gradients, stabilizing the training process.
• Theoretical conditions for GAF are met, proving its capability to alleviate ill-conditioning, vanishing/exploding gradients, and saddle point problems.
• Experiments on CIFAR, ImageNet, and PASCAL datasets demonstrate significant performance improvements and faster convergence rates for DNNs trained with GAF.

Conclusions:

• The proposed Gradient Activation Function (GAF) is a viable method for enhancing the training of deep neural networks.
• GAF demonstrates effectiveness in improving model performance and convergence speed across diverse deep learning architectures and datasets.
• The GAF method is broadly applicable and can be integrated into various deep neural networks to boost their overall effectiveness.