Pith. sign in

REVIEW 1 cited by

A Novel Convolutional Neural Network Architecture with a Continuous Symmetry

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

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 2308.01621 v4 pith:7QD2SDR3 submitted 2023-08-03 cs.CV cs.LGcs.NE

Yao Liu , Hang Shao , Bing Bai This is my paper · ORCID
classification cs.CVcs.LGcs.NE
keywords architecturenetworkneuralsymmetrycontinuousconvolutionalweightsallows
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

This paper introduces a new Convolutional Neural Network (ConvNet) architecture inspired by a class of partial differential equations (PDEs) called quasi-linear hyperbolic systems. With comparable performance on the image classification task, it allows for the modification of the weights via a continuous group of symmetry. This is a significant shift from traditional models where the architecture and weights are essentially fixed. We wish to promote the (internal) symmetry as a new desirable property for a neural network, and to draw attention to the PDE perspective in analyzing and interpreting ConvNets in the broader Deep Learning community.

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. STA-Net: A Decoupled Shape and Texture Attention Network for Lightweight Plant Disease Classification

    cs.CV 2025-09 conditional novelty 4.0 of 10

    STA-Net, a 401K-parameter model with a decoupled shape-texture attention module, reaches 89.00% accuracy and 88.96% F1 on the CCMT plant disease dataset.

Pith tools