Adaptive Parametric Activation

Konstantinos Panagiotis Alexandridis

Keywords: Vision and Learning

TL;DR: A novel activation function that aligns the network's activations with the data distribution, unifies most previous activations functions and achieves great performance in imbalanced and balanced classification and detection tasks.

Abstract:

The activation function plays a crucial role in model optimisation, yet the optimal choice remains unclear. For example, the Sigmoid activation is the de-facto activation in balanced classification tasks, however, in imbalanced classification, it proves inappropriate due to bias towards frequent classes. In this work, we delve deeper in this phenomenon by performing a comprehensive statistical analysis in the classification and intermediate layers of both balanced and imbalanced networks and we empirically show that aligning the activation function with the data distribution, enhances the performance in both balanced and imbalanced tasks. To this end, we propose the Adaptive Parametric Activation (APA) function, a novel and versatile activation function that unifies most common activation functions under a single formula. APA can be applied in both intermediate layers and attention layers, significantly outperforming the state-of-the-art on several imbalanced benchmarks such as ImageNet-LT, iNaturalist2018, Places-LT, CIFAR100-LT and LVIS and balanced benchmarks such as ImageNet1K, COCO and V3DET. The code is available at https://github.com/kostas1515/AGLU.

Oral at ECCV2024: https://eccv.ecva.net/virtual/2024/oral/486

Keywords: Vision and Learning

Submission Number: 39