Pith. sign in

REVIEW 1 cited by

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

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1701.07148 v1 pith:CC74K3EO submitted 2017-01-25 cs.LG

classification cs.LG
keywords methodcompressionconvolutionalcostcp-decompositiondecomposinglayermemory
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Factorized Implicit Global Convolution for Automotive Computational Fluid Dynamics Prediction

    cs.CV 2025-02 conditional novelty 6.0 of 10

    FIGConv uses factorized 3D grids and global convolutions to predict car surface pressure and drag, reporting R2=0.957 on DrivAerNet drag and 0.89% pressure error on Ahmed body.

Pith tools