Fine-Pruning: A Biologically Inspired Algorithm for Personalization of Machine Learning Models

2026-02-18

AI Summary

This work proposes a biologically inspired, unsupervised personalization method that eliminates the need for backpropagation and labeled data, addressing the inefficiency of conventional deep neural networks in resource-constrained settings. Drawing inspiration from neural pruning mechanisms in the brain, the approach introduces a fine-grained, plasticity-driven pruning strategy into mainstream architectures such as ResNet-50 for the first time. Evaluated on benchmarks including ImageNet, the method achieves approximately 70% sparsity while boosting accuracy to around 90%, substantially reducing computational overhead. This demonstrates a notable departure from the typical trade-off between model efficiency and performance, as the technique simultaneously lowers resource consumption and enhances predictive accuracy.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Computer Vision: Learning & Optimization for CV
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Neural networks have long strived to emulate the learning capabilities of the human brain. While deep neural networks (DNNs) draw inspiration from the brain in neuron design, their training methods diverge from biological foundations. Backpropagation, the primary training method for DNNs, requires substantial computational resources and fully labeled datasets, presenting major bottlenecks in development and application. This work demonstrates that by returning to biomimicry, specifically mimicking how the brain learns through pruning, we can solve various classical machine learning problems while utilizing orders of magnitude fewer computational resources and no labels. Our experiments successfully personalized multiple speech recognition and image classification models, including ResNet50 on ImageNet, resulting in increased sparsity of approximately 70\% while simultaneously improving model accuracy to around 90\%, all without the limitations of backpropagation. This biologically inspired approach offers a promising avenue for efficient, personalized machine learning models in resource-constrained environments.

Problem

• personalization
• backpropagation
• biological inspiration
• resource-constrained learning
• label-free learning

Innovation

• Fine-Pruning
• biologically inspired
• model personalization
• pruning
• label-free learning