Scale-invariant Gaussian derivative residual networks
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Whole slide images (WSIs) in digital histopathology are acquired at discrete magnification levels encoding complementary diagnostic information from global tissue architecture to fine-grained cellular morphology. Yet, deep learning models remain sensitive to scale variation.
arXiv:2505. 04397v2 Announce Type: replace-cross Abstract: Modern vision networks are dominated by additive local transformations, whereas explicit multiplicative local interactions remain underexplored.
Over the past decade, deep neural networks (DNNs) have achieved remarkable success on complex machine-learning tasks, yet the theoretical foundations of their performance remain incomplete. From a statistical viewpoint, a natural question is: can DNNs attain feature-learning and prediction consistency comparable to that of classical models?
arXiv:2403.04545v4 Announce Type: replace Abstract: Scaling factors in residual branches have emerged as a prevalent method for boosting neural network performance, especially in normalization-free a...
GradAttn replaces fixed residual connections in deep ConvNets with attention‑controlled gradient pathways, allowing the network to dynamically weight shallow texture features and deep semantic representations. The method extracts multi‑scale CNN features at different depths and regulates them through self‑attention, leading to improved performance over ResNet‑18 on five of eight evaluated datasets, including a +11.07% accuracy gain on FashionMNIST. Analysis of gradient flow shows that controlled instabilities introduced by attention can coincide with better generalization, while positional encoding proves to be dataset‑dependent.
arXiv:2606. 29400v1 Announce Type: cross Abstract: In computer graphics, visual content is continuously warped, zoomed and resampled.