mlsanityData to 14 Oct 2026

Tensor Shape Search for Optimum Data Compression

arXiv 2205.10651 ↗First posted 21 May 2022eess.IV cs.LG cs.NEcited by 0

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Abstract (arXiv)

Various tensor decomposition methods have been proposed for data compression. In real world applications of the tensor decomposition, selecting the tensor shape for the given data poses a challenge and the shape of the tensor may affect the error and the compression ratio. In this work, we study the effect of the tensor shape on the tensor decomposition and propose an optimization model to find an optimum shape for the tensor train (TT) decomposition. The proposed optimization model maximizes the compression ratio of the TT decomposition given an error bound. We implement a genetic algorithm (GA) linked with the TT-SVD algorithm to solve the optimization model. We apply the proposed method for the compression of RGB images. The results demonstrate the effectiveness of the proposed evolutionary tensor shape search for the TT decomposition.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 30 references that are in the map. First posted on arXiv 21 May 2022.

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13 of 30 references.

References · 13

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  2. 2014Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decompositioncited by 203
  3. 2015Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applicationscited by 162
  4. 2019Model Compression with Adversarial Robustness: A Unified Optimization Frameworkcited by 71
  5. 2014Bayesian CP Factorization of Incomplete Tensors with Automatic Rank Determinationcited by 65
  6. 2018Wide Compression: Tensor Ring Netscited by 50
  7. 2019Bayesian Tensorized Neural Networks with Automatic Rank Selectioncited by 26
  8. 2020Towards Compact Neural Networks via End-to-End Training: A Bayesian Tensor Approach with Automatic Rank Determinationcited by 18
  9. 2020Understanding Generalization in Deep Learning via Tensor Methodscited by 14
  10. 2021On-FPGA Training with Ultra Memory Reduction: A Low-Precision Tensor Methodcited by 9
  11. 2015Bayesian Sparse Tucker Models for Dimension Reduction and Tensor Completioncited by 8
  12. 2014Enabling High-Dimensional Hierarchical Uncertainty Quantification by ANOVA and Tensor-Train Decompositioncited by 4
1 more
  1. 2021High-Dimensional Uncertainty Quantification via Tensor Regression with Rank Determination and Adaptive Samplingcited by 1

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