arXiv (All)
open access
Layerwise Tunable Lifting Scheme for the Convolutional Neural Network
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 that jointly adapts low- and high-frequency branches (LS-LayLatt-Seq
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Understanding wavelet analysis and filters for engineering applications#958226
- Fast and Memory-Efficient Wavelet Convolutions via I/O-Aware Reformulation#444696
- Anon: Extrapolating Adaptivity Beyond SGD and Adam#1062898
- Coarse-to-fine multi-resolution hash encoding for implicit full waveform inversion#1156016
- Application of the wavelet transform in turbulence#1005208
Record · ID 996646
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive