Orthogonal Residual Updates enhance feature learning and training stability by decomposing module outputs to contribute primarily novel features.
Residual connections are pivotal for deep neural networks, enabling greater
depth by mitigating vanishing gradients. However, in standard residual updates,
the module’s output is directly added to the input stream. This can lead to
updates that predominantly reinforce or modulate the existing stream direction,
potentially underutilizing the module’s capacity for learning entirely novel
features. In this work, we introduce Orthogonal Residual Update: we decompose
the module’s output relative to the input stream and add only the component
orthogonal to this stream. This design aims to guide modules to contribute
primarily new representational directions, fostering richer feature learning
while promoting more efficient training. We demonstrate that our orthogonal
update strategy improves generalization accuracy and training stability across
diverse architectures (ResNetV2, Vision Transformers) and datasets (CIFARs,
TinyImageNet, ImageNet-1k), achieving, for instance, a +4.3\%p top-1 accuracy
gain for ViT-B on ImageNet-1k.