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Found 8 papers, 2 papers with code
A Learnable ScatterNet: Locally Invariant Convolutional Layers
We do this by breaking down the scattering orders into single convolutional-like layers we call 'locally invariant' layers, and adding a learned mixing term to this layer.
Deep Learning in the Wavelet Domain
We are stimulated by both Mallat's scattering transform and the idea of filtering in the Fourier domain.
Generative ScatterNet Hybrid Deep Learning (G-SHDL) Network with Structural Priors for Semantic Image Segmentation
This paper proposes a generative ScatterNet hybrid deep learning (G-SHDL) network for semantic image segmentation.
Visualizing and Improving Scattering Networks
These complex patterns may be useful for texture classification, but are quite dissimilar from the patterns visualized in second and third layers of Convolutional Neural Networks (CNNs) - the current state of the art Image Classifiers.
ScatterNet Hybrid Deep Learning (SHDL) Network For Object Classification
The paper proposes the ScatterNet Hybrid Deep Learning (SHDL) network that extracts invariant and discriminative image representations for object recognition.
Efficient Convolutional Network Learning using Parametric Log based Dual-Tree Wavelet ScatterNet
The ScatterNet extracts edge based invariant representations that are used by the later layers of the CNN to learn high-level features.
Multi-Resolution Dual-Tree Wavelet Scattering Network for Signal Classification
This paper introduces a Deep Scattering network that utilizes Dual-Tree complex wavelets to extract translation invariant representations from an input signal.
Dual-Tree Wavelet Scattering Network with Parametric Log Transformation for Object Classification
We introduce a ScatterNet that uses a parametric log transformation with Dual-Tree complex wavelets to extract translation invariant representations from a multi-resolution image.
