Competition-based Adaptive ReLU for Deep Neural Networks (2407.19441v1)

Abstract: Activation functions introduce nonlinearity into deep neural networks. Most popular activation functions allow positive values to pass through while blocking or suppressing negative values. From the idea that positive values and negative values are equally important, and they must compete for activation, we proposed a new Competition-based Adaptive ReLU (CAReLU). CAReLU scales the input values based on the competition results between positive values and negative values. It defines two parameters to adjust the scaling strategy and can be trained uniformly with other network parameters. We verify the effectiveness of CAReLU on image classification, super-resolution, and natural language processing tasks. In the experiment, our method performs better than other widely used activation functions. In the case of replacing ReLU in ResNet-18 with our proposed activation function, it improves the classification accuracy on the CIFAR-100 dataset. The effectiveness and the new perspective on the utilization of competition results between positive values and negative values make CAReLU a promising activation function.

• The paper introduces CAReLU, a novel activation function that adaptively balances positive and negative inputs through a competition-based scaling mechanism.
• It employs diverse indicators, including energy-based metrics, to optimize parameter tuning, resulting in improved classification accuracy and higher PSNR in image super-resolution.
• Empirical evaluations on standard datasets like CIFAR-100 and BSD300 demonstrate CAReLU's practical efficacy and potential for enhancing deep neural network performance.

Competition-Based Adaptive ReLU for Deep Neural Networks

In the context of deep learning, activation functions are pivotal for introducing nonlinearity, which allows neural networks to approximate complex functions. Among the most widely adopted activation functions is the Rectified Linear Unit (ReLU), which permits only positive inputs to pass while discarding negative ones. This paper proposes a novel activation function, the Competition-based Adaptive ReLU (CAReLU), which suggests that both positive and negative values should compete for activation, grounded in the notion that they carry equivalent significance in processing.

Key Contributions

The primary contribution of this research is the introduction of CAReLU, an adaptive version of ReLU that scales input values based on a competitive mechanism between positive and negative inputs. The competition's outcome determines the operational parameters of the CAReLU which are optimized alongside the network's weights. The key components of CAReLU include:

1. Adaptive Scaling: CAReLU defines a competition-based scaling mechanism, introducing parameters that train concurrently with other neural network parameters to adaptively balance the emphasis on positive versus negative activations.
2. Flexibility in Competition Indicators: The method explores diverse indicators to gauge competition, such as energy of input values, offering adaptability across different applications.
3. Empirical Superiority: CAReLU is empirically validated on a variety of tasks, demonstrating superior performance in image classification, image super-resolution, and natural language inference.

Experimental Analysis

The research conducts extensive experiments utilizing CAReLU on standard datasets. Results indicate that replacing conventional ReLU with CAReLU in architectures like ResNet-18 and VGG-13 improves classification accuracy on CIFAR-100. CAReLU exhibits higher Peak Signal-to-Noise Ratio (PSNR) in the Berkeley Segmentation Dataset (BSD300) image super-resolution task, reaffirming its practical efficacy across diverse settings.

Methodologically, the paper provides insights into the behavior of CAReLU's scaling decisions concerning parameter tuning and initial configuration, illustrating how these factors contribute to its robustness. The experimental findings suggest that among the different implementations—using energy ratio, L1 norm, and count of positive inputs—the energy-based version, CAReLU_E, generally performs best.

Implications and Future Directions

The CAReLU approach reimagines activation function design by considering inputs' positive and negative components equivalently significant rather than suppressing one entirely. This perspective opens up avenues for leveraging competition mechanisms in enhancing network adaptivity and expressiveness.

Future research may explore extending this competitive approach to multi-modal inputs or complex neural architectures like transformers. Additionally, investigating the potential for CAReLU to enhance optimization stability in large-scale neural networks could yield further advancements in model performance and convergence behavior.

Overall, this paper proposes a compelling new activation function paradigm that could impact the theoretical and practical aspects of deep learning, inviting the exploration of similar adaptive techniques in other neural network components.