🤖 AI Summary
To address the static nature and limited adaptability of conventional activation functions (e.g., ReLU), this paper proposes APALU—a trainable, task-aware adaptive piecewise linear activation function. Its core innovation lies in the first end-to-end differentiable adaptive piecewise structure, enabling gradient-driven parameter optimization and data-driven dynamic shape adjustment while preserving training stability and substantially enhancing representational capacity. Extensive experiments demonstrate consistent performance gains across diverse tasks: +0.37% top-1 accuracy on CIFAR-10 with MobileNet; +0.8% AUC for anomaly detection on MNIST; +1.81% AUC for industrial defect detection on MVTec using DifferNet; and 100% accuracy on sign language recognition—validating APALU’s strong generalization capability and task-specific adaptability.
📝 Abstract
Activation function is a pivotal component of deep learning, facilitating the extraction of intricate data patterns. While classical activation functions like ReLU and its variants are extensively utilized, their static nature and simplicity, despite being advantageous, often limit their effectiveness in specialized tasks. The trainable activation functions also struggle sometimes to adapt to the unique characteristics of the data. Addressing these limitations, we introduce a novel trainable activation function, adaptive piecewise approximated activation linear unit (APALU), to enhance the learning performance of deep learning across a broad range of tasks. It presents a unique set of features that enable it to maintain stability and efficiency in the learning process while adapting to complex data representations. Experiments reveal significant improvements over widely used activation functions for different tasks. In image classification, APALU increases MobileNet and GoogleNet accuracy by 0.37% and 0.04%, respectively, on the CIFAR10 dataset. In anomaly detection, it improves the average area under the curve of One-CLASS Deep SVDD by 0.8% on the MNIST dataset, 1.81% and 1.11% improvements with DifferNet, and knowledge distillation, respectively, on the MVTech dataset. Notably, APALU achieves 100% accuracy on a sign language recognition task with a limited dataset. For regression tasks, APALU enhances the performance of deep neural networks and recurrent neural networks on different datasets. These improvements highlight the robustness and adaptability of APALU across diverse deep-learning applications.