# DA$^{\\textbf{2}}$-Net : Diverse & Adaptive Attention Convolutional Neural Network Source: [DA$^{\\textbf{2}}$-Net : Diverse & Adaptive Attention Convolutional Neural Network]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:11:34.522Z Authors: Abenezer Girma, Abdollah Homaifar, M Nabil Mahmoud, Xuyang Yan, Mrinmoy Sarkar Published: 2021-11-25T16:25:16.000Z ## Abstract Standard Convolutional Neural Network (CNN) designs rarely focus on the importance of explicitly capturing diverse features to enhance the network's performance. Instead, most existing methods follow an indirect approach of increasing or tuning the networks' depth and width, which in many cases significantly increases the computational cost. Inspired by a biological visual system, we propose a Diverse and Adaptive Attention Convolutional Network (DA$^{2}$-Net), which enables any feed-forward CNNs to explicitly capture diverse features and adaptively select and emphasize the most informative features to efficiently boost the network's performance. DA$^{2}$-Net incurs negligible computational overhead and it is designed to be easily integrated with any CNN architecture. We extensively evaluated DA$^{2}$-Net on benchmark datasets, including CIFAR100, SVHN, and ImageNet, with various CNN architectures. The experimental results show DA$^{2}$-Net provides a significant performance improvement with very minimal computational overhead. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()