# CP-decomposition with Tensor Power Method for Convolutional Neural Networks Compression

arXiv: https://arxiv.org/abs/1701.07148 · HTML: https://mlsanity.com/paper/1701.07148/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2017.

## Built on by (most cited first, 30 of 30)

- [Tensor Methods in Computer Vision and Deep Learning](https://mlsanity.com/paper/2107.03436/) (2021)
- [HALOC: Hardware-Aware Automatic Low-Rank Compression for Compact Neural Networks](https://mlsanity.com/paper/2301.09422/) (2023)
- [Tensor Decomposition for Model Reduction in Neural Networks: A Review](https://mlsanity.com/paper/2304.13539/) (2023)
- [A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services](https://mlsanity.com/paper/2304.14271/) (2023)
- [Geometry-aware training of factorized layers in tensor Tucker format](https://mlsanity.com/paper/2305.19059/) (2023)
- [Low-rank Tensor Decomposition for Compression of Convolutional Neural Networks Using Funnel Regularization](https://mlsanity.com/paper/2112.03690/) (2021)
- [Low Rank Optimization for Efficient Deep Learning: Making A Balance between Compact Architecture and Fast Training](https://mlsanity.com/paper/2303.13635/) (2023)
- [Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks](https://mlsanity.com/paper/2505.20132/) (2025)
- [Position: Tensor Networks are a Valuable Asset for Green AI](https://mlsanity.com/paper/2205.12961/) (2022)
- [Canonical convolutional neural networks](https://mlsanity.com/paper/2206.01509/) (2022)
- [MIGS: Multi-Identity Gaussian Splatting via Tensor Decomposition](https://mlsanity.com/paper/2407.07284/) (2024)
- [Parameter-efficient Bayesian Neural Networks for Uncertainty-aware Depth Estimation](https://mlsanity.com/paper/2409.17085/) (2024)
- [Efficient Visual Recognition with Deep Neural Networks: A Survey on Recent Advances and New Directions](https://mlsanity.com/paper/2108.13055/) (2021)
- [Robust Adversarial Defense by Tensor Factorization](https://mlsanity.com/paper/2309.01077/) (2023)
- [Boosting Defect Detection in Manufacturing using Tensor Convolutional Neural Networks](https://mlsanity.com/paper/2401.01373/) (2023)
- [Exploiting Elasticity in Tensor Ranks for Compressing Neural Networks](https://mlsanity.com/paper/2105.04218/) (2021)
- [Defensive Tensorization](https://mlsanity.com/paper/2110.13859/) (2021)
- [A New Measure of Model Redundancy for Compressed Convolutional Neural Networks](https://mlsanity.com/paper/2112.04857/) (2021)
- [Design Automation for Fast, Lightweight, and Effective Deep Learning Models: A Survey](https://mlsanity.com/paper/2208.10498/) (2022)
- [When Parameter-efficient Tuning Meets General-purpose Vision-language Models](https://mlsanity.com/paper/2312.12458/) (2023)
- [Nonlinear Tensor Ring Network](https://mlsanity.com/paper/2111.06532/) (2021)
- [Selective Multiple Power Iteration: from Tensor PCA to gradient-based exploration of landscapes](https://mlsanity.com/paper/2112.12306/) (2021)
- [Lightweight Attribute Localizing Models for Pedestrian Attribute Recognition](https://mlsanity.com/paper/2306.09822/) (2023)
- [Convolutional Neural Network Compression via Dynamic Parameter Rank Pruning](https://mlsanity.com/paper/2401.08014/) (2024)
- [Reduced storage direct tensor ring decomposition for convolutional neural networks compression](https://mlsanity.com/paper/2405.10802/) (2024)
- [Layer-Specific Optimization: Sensitivity Based Convolution Layers Basis Search](https://mlsanity.com/paper/2408.06024/) (2024)
- [Efficient CNN Compression via Multi-method Low Rank Factorization and Feature Map Similarity](https://mlsanity.com/paper/2510.00062/) (2025)
- [Distribution-Aware Tensor Decomposition for Compression of Convolutional Neural Networks](https://mlsanity.com/paper/2511.04494/) (2025)
- [Optimizing Tensor Train Decomposition in DNNs for RISC-V Architectures Using Design Space Exploration and Compiler Optimizations](https://mlsanity.com/paper/2602.01996/) (2026)
- [Fast Tensorization of Neural Networks via Slice-wise Feature Distillation](https://mlsanity.com/paper/2605.19842/) (2026)

## Abstract (arXiv)

Convolutional Neural Networks (CNNs) has shown a great success in many areas including complex image classification tasks. However, they need a lot of memory and computational cost, which hinders them from running in relatively low-end smart devices such as smart phones. We propose a CNN compression method based on CP-decomposition and Tensor Power Method. We also propose an iterative fine tuning, with which we fine-tune the whole network after decomposing each layer, but before decomposing the next layer. Significant reduction in memory and computation cost is achieved compared to state-of-the-art previous work with no more accuracy loss.