ILPO-NET: Network for the invariant recognition of arbitrary volumetric patterns in 3D

• URL: http://arxiv.org/abs/2403.19612v3
• Date: Wed, 24 Apr 2024 14:26:52 GMT
• Title: ILPO-NET: Network for the invariant recognition of arbitrary volumetric patterns in 3D
• Authors: Dmitrii Zhemchuzhnikov, Sergei Grudinin,
• Abstract summary: We present ILPO-Net, a novel approach that handles arbitrarily shaped patterns with the convolutional operation inherently invariant to local spatial pattern orientations. Our architecture seamlessly integrates the new convolution operator and, when benchmarked on diverse volumetric datasets such as MedMNIST and CATH, demonstrates superior performance. Our code is publicly available at https://gricad.gitlab.univ-grenoble-alpes.fr/GruLab/ILPO/-/tree/main/ILPONet.
• Score: 2.7624021966289605
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Effective recognition of spatial patterns and learning their hierarchy is crucial in modern spatial data analysis. Volumetric data applications seek techniques ensuring invariance not only to shifts but also to pattern rotations. While traditional methods can readily achieve translational invariance, rotational invariance possesses multiple challenges and remains an active area of research. Here, we present ILPO-Net (Invariant to Local Patterns Orientation Network), a novel approach that handles arbitrarily shaped patterns with the convolutional operation inherently invariant to local spatial pattern orientations using the Wigner matrix expansions. Our architecture seamlessly integrates the new convolution operator and, when benchmarked on diverse volumetric datasets such as MedMNIST and CATH, demonstrates superior performance over the baselines with significantly reduced parameter counts - up to 1000 times fewer in the case of MedMNIST. Beyond these demonstrations, ILPO-Net's rotational invariance paves the way for other applications across multiple disciplines. Our code is publicly available at https://gricad-gitlab.univ-grenoble-alpes.fr/GruLab/ILPO/-/tree/main/ILPONet.

Related papers

• On the Fourier analysis in the SO(3) space : EquiLoPO Network [2.7624021966289605]
  Existing deep-learning approaches utilize either group convolutional networks limited to discrete rotations or steerable convolutional networks with constrained filter structures. This work proposes a novel equivariant neural network architecture that achieves analytical Equivariance to Local Pattern Orientation on the continuous SO(3) group. By integrating these operations into a ResNet-style architecture, we propose a model that overcomes the limitations of prior methods.
  arXiv  Detail & Related papers  (2024-04-24T16:54:39Z)
• Deep Neural Networks with Efficient Guaranteed Invariances [77.99182201815763]
  We address the problem of improving the performance and in particular the sample complexity of deep neural networks. Group-equivariant convolutions are a popular approach to obtain equivariant representations. We propose a multi-stream architecture, where each stream is invariant to a different transformation.
  arXiv  Detail & Related papers  (2023-03-02T20:44:45Z)
• A Simple Strategy to Provable Invariance via Orbit Mapping [14.127786615513978]
  We propose a method to make network architectures provably invariant with respect to group actions. In a nutshell, we intend to 'undo' any possible transformation before feeding the data into the actual network.
  arXiv  Detail & Related papers  (2022-09-24T03:40:42Z)
• SVNet: Where SO(3) Equivariance Meets Binarization on Point Cloud Representation [65.4396959244269]
  The paper tackles the challenge by designing a general framework to construct 3D learning architectures. The proposed approach can be applied to general backbones like PointNet and DGCNN. Experiments on ModelNet40, ShapeNet, and the real-world dataset ScanObjectNN, demonstrated that the method achieves a great trade-off between efficiency, rotation, and accuracy.
  arXiv  Detail & Related papers  (2022-09-13T12:12:19Z)
• Improving the Sample-Complexity of Deep Classification Networks with Invariant Integration [77.99182201815763]
  Leveraging prior knowledge on intraclass variance due to transformations is a powerful method to improve the sample complexity of deep neural networks. We propose a novel monomial selection algorithm based on pruning methods to allow an application to more complex problems. We demonstrate the improved sample complexity on the Rotated-MNIST, SVHN and CIFAR-10 datasets.
  arXiv  Detail & Related papers  (2022-02-08T16:16:11Z)
• Scaling Structured Inference with Randomization [64.18063627155128]
  We propose a family of dynamic programming (RDP) randomized for scaling structured models to tens of thousands of latent states. Our method is widely applicable to classical DP-based inference. It is also compatible with automatic differentiation so can be integrated with neural networks seamlessly.
  arXiv  Detail & Related papers  (2021-12-07T11:26:41Z)
• Dynamic Convolution for 3D Point Cloud Instance Segmentation [146.7971476424351]
  We propose an approach to instance segmentation from 3D point clouds based on dynamic convolution. We gather homogeneous points that have identical semantic categories and close votes for the geometric centroids. The proposed approach is proposal-free, and instead exploits a convolution process that adapts to the spatial and semantic characteristics of each instance.
  arXiv  Detail & Related papers  (2021-07-18T09:05:16Z)
• Gram Regularization for Multi-view 3D Shape Retrieval [3.655021726150368]
  We propose a novel regularization term called Gram regularization. By forcing the variance between weight kernels to be large, the regularizer can help to extract discriminative features. The proposed Gram regularization is data independent and can converge stably and quickly without bells and whistles.
  arXiv  Detail & Related papers  (2020-11-16T05:37:24Z)
• Regularizing Deep Networks with Semantic Data Augmentation [44.53483945155832]
  We propose a novel semantic data augmentation algorithm to complement traditional approaches. The proposed method is inspired by the intriguing property that deep networks are effective in learning linearized features. We show that the proposed implicit semantic data augmentation (ISDA) algorithm amounts to minimizing a novel robust CE loss.
  arXiv  Detail & Related papers  (2020-07-21T00:32:44Z)
• Invariant Integration in Deep Convolutional Feature Space [77.99182201815763]
  We show how to incorporate prior knowledge to a deep neural network architecture in a principled manner. We report state-of-the-art performance on the Rotated-MNIST dataset.
  arXiv  Detail & Related papers  (2020-04-20T09:45:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.