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New tunable lifting schemes improve ResNet-18 performance in image tasks

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new family of tunable lifting schemes for biorthogonal wavelet filter banks, offering three distinct strategies for adapting low-pass, high-pass, or both frequency branches. These schemes are designed using a lattice-based structure to ensure invertibility and stability. When integrated into a ResNet-18 model for image classification and anomaly detection tasks, the proposed methods demonstrated consistent performance improvements across various datasets. AI

IMPACT Introduces novel techniques for improving image classification and anomaly detection models.

RANK_REASON The cluster contains an academic paper detailing a new methodology for convolutional neural networks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New tunable lifting schemes improve ResNet-18 performance in image tasks

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The cluster contains an academic paper detailing a new methodology for convolutional neural networks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Abdumannon Yovkochov, An Le, Sungbal Seo, You-Suk Bae, Truong Nguyen ·

    Layerwise Tunable Lifting Scheme for the Convolutional Neural Network

    arXiv:2609.09827v1 Announce Type: new Abstract: This work introduces a family of tunable lifting schemes for biorthogonal wavelet filter banks. We propose three lifting strategies: low-pass tuning (LS-LayLatt-LP), high-pass tuning (LS-LayLatt-HP), and a sequential lifting scheme …

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