# Lightweight Adaptive ReduNet via Hyperspherical Manifold Learning

arXiv: https://arxiv.org/abs/2608.20668 · HTML: https://mlsanity.com/paper/2608.20668/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 26 references that are in the map.
- First posted on arXiv 21 Aug 2026.

## Builds on (most cited first, 2 of 2)

- [CINIC-10 is not ImageNet or CIFAR-10](https://mlsanity.com/paper/1810.03505/) (2018)
- [ESS-ReduNet: Enhancing Subspace Separability of ReduNet via Dynamic Expansion with Bayesian Inference](https://mlsanity.com/paper/2411.17961/) (2024)

## Abstract (arXiv)

In recent years, a white-box neural network called ReduNet has been proposed, which employs the maximal coding rate reduction (MCR2) principle to transform raw data into low-dimensional discriminative features via a forward layer-wise construction process. Unlike traditional deep networks that rely on backpropagation, ReduNet explicitly derives the parameters of each layer from the features of its preceding layer, offering a mathematically interpretable paradigm. However, this layer-wise construction often requires a large number of layers for the MCR2 objective to reach a stable value, which increases the parameter storage of the unfolded module. To address this issue, we propose LA-ReduNet, a lightweight adaptive architecture that refines the layer-wise update rule and enables discriminative feature representations to be obtained with substantially fewer unfolded layers. Specifically, LA-ReduNet employs hyperspherical manifold learning and adaptive step sizes, thereby reducing by an order of magnitude the number of layers required for the MCR2 objective to reach a stable value. Simulation results demonstrate that, while maintaining comparable classification accuracy, LA-ReduNet requires significantly fewer layers for the MCR2 objective to reach a stable value. Remarkably, under the considered experimental settings, LA-ReduNet requires only approximately 1/29 of the parameter storage of the unfolded ReduNet module for the MCR2 objective to reach a stable value.