CosineGate: Semantic Dynamic Routing via Cosine Incompatibility in Residual Networks
Yogeswar Reddy Thota

TL;DR
CosineGate introduces a dynamic residual routing mechanism using cosine incompatibility to reduce computation in deep networks, achieving high accuracy and efficiency without auxiliary supervision or heuristics.
Contribution
It proposes a novel, differentiable architecture that uses cosine incompatibility as a self-supervised signal for dynamic routing in residual networks.
Findings
Achieves 89.9% accuracy with 24.1% FLOPs savings on CIFAR-10.
Matches or exceeds ResNet-20 accuracy while reducing computation.
Demonstrates geometric feature incompatibility as an effective routing signal.
Abstract
Modern deep residual networks perform substantial redundant computation by evaluating all residual blocks for every input, even when identity mappings suffice. We introduce CosineGate, an end-to-end differentiable architecture for dynamic routing in residual networks that uses cosine incompatibility between identity and residual feature representations as a self-supervised skip signal. CosineGate measures semantic redundancy through the Cosine Incompatibility Ratio (CIR), defined as 1 - cos(x, F(x)), and uses Gumbel-Softmax relaxation to enable per-sample, per-block gating during training. A progressive FLOPs regularization term controls average compute usage without destabilizing optimization. On CIFAR-10, CosineGate spans the accuracy-efficiency Pareto frontier: an aggressive configuration achieves 89.9 percent accuracy with 24.1 percent FLOPs savings, a balanced configuration…
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Taxonomy
TopicsAdvanced Neural Network Applications · Adversarial Robustness in Machine Learning · Advanced Graph Neural Networks
