● “Lightweight Pixel Difference Networks for Efficient Visual Representation Learning (Su et al., 2024)” is a paper that proposes a lightweight version of the Pixel Difference Network for edge device-related vision models.
● The paper aims to make convolutional networks more efficient while maintaining performance by addressing issues such as computational complexity and expressive power.
● The expressive power of CNNs comes from network depth and convolution, and these can be enhanced by capturing higher-order local differential information effectively.
● The proposed Pixel Difference CNN (PDC) aims to combine the advantages of existing CNN-based models while capturing higher-order information more effectively through different local binary pattern (LBP) strategies.
● The PDC offers an effective way of representing images by combining existing CNN-based models with effective local descriptors to capture higher-order information more effectively.
Author: Aria Lee