Convolutional Analysis Operator Learning: Acceleration and Convergence.

ABSTRACT: Convolutional operator learning is gaining attention in many signal processing and computer vision applications. Learning kernels has mostly relied on so-called patch-domain approaches that extract and store many overlapping patches across training signals. Due to memory demands, patch-domain methods have limitations when learning kernels from large datasets - particularly with multi-layered structures, e.g., convolutional neural networks - or when applying the learned kernels to high-dimensional signal recovery problems. The so-called convolution approach does not store many overlapping patches, and thus overcomes the memory problems particularly with careful algorithmic designs; it has been studied within the "synthesis" signal model, e.g., convolutional dictionary learning. This paper p

SUBMITTER: Chun IY 

PROVIDER: S-EPMC7170176 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature