Parametric Scattering Networks

Gauthier, Shanel; Thérien, Benjamin; Alsène-Racicot, Laurent; Rish, Irina; Belilovsky, Eugene; Eickenberg, Michael; Wolf, Guy

doi:10.1109/cvpr52688.2022.00566

Cited by 9 publications

(2 citation statements)

References 25 publications

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“…The novel layer is applied at different depths of a CNN and works best, when it is used in an early, but not the first layer of the CNN. Gauthier et al (2022) directly learn the parameters of the scattering transform's Mother wavelet via back-propagation instead of using a filterbank with hand-crafted wavelets. Thereby, the design of the wavelet family has relaxed constraints, granting problemdependent flexibility.…”

Section: In-and Equivariant Dnnsmentioning

confidence: 99%

Boosting deep neural networks with geometrical prior knowledge: a survey

Rath,

Condurache

2024

Artif Intell Rev

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Deep neural networks achieve state-of-the-art results in many different problem settings by exploiting vast amounts of training data. However, collecting, storing and—in the case of supervised learning—labelling the data is expensive and time-consuming. Additionally, assessing the networks’ generalization abilities or predicting how the inferred output changes under input transformations is complicated since the networks are usually treated as a black box. Both of these problems can be mitigated by incorporating prior knowledge into the neural network. One promising approach, inspired by the success of convolutional neural networks in computer vision tasks, is to incorporate knowledge about symmetric geometrical transformations of the problem to solve that affect the output in a predictable way. This promises an increased data efficiency and more interpretable network outputs. In this survey, we try to give a concise overview about different approaches that incorporate geometrical prior knowledge into neural networks. Additionally, we connect those methods to 3D object detection for autonomous driving, where we expect promising results when applying those methods.

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Section: In-and Equivariant Dnnsmentioning

confidence: 99%

Boosting deep neural networks with geometrical prior knowledge: a survey

Rath,

Condurache

2024

Artif Intell Rev

View full text Add to dashboard Cite

show abstract

Section: Scattering Neural Networkmentioning

confidence: 99%

Boosting Deep Neural Networks with Geometrical Prior Knowledge: A Survey

Rath

Condurache

2023

Preprint

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Deep Neural Networks achieve state-of-the-art results in many different problem settings by exploiting vast amounts of training data. However, collecting, storing and - in the case of supervised learning - labelling the data is expensive and time-consuming. Additionally, assessing the networks' generalization abilities or predicting how the inferred output changes under input transformations is complicated since the networks are usually treated as a black box. Both of these problems can be mitigated by incorporating prior knowledge into the neural network. One promising approach, inspired by the success of convolutional neural networks in computer vision tasks, is to incorporate knowledge about symmetric geometrical transformations of the problem to solve that affect the output in a predictable way. This promises an increased data efficiency and more interpretable network outputs. In this survey, we try to give a concise overview about different approaches that incorporate geometrical prior knowledge into neural networks. Additionally, we connect those methods to 3D object detection for autonomous driving, where we expect promising results when applying those methods.

show abstract