OneDConv: Generalized Convolution For Transform-Invariant Representation

• URL: http://arxiv.org/abs/2201.05781v1
• Date: Sat, 15 Jan 2022 07:44:44 GMT
• Title: OneDConv: Generalized Convolution For Transform-Invariant Representation
• Authors: Tong Zhang, Haohan Weng, Ke Yi, C. L. Philip Chen
• Abstract summary: We propose a novel generalized one dimension convolutional operator (OneDConv) It dynamically transforms the convolution kernels based on the input features in a computationally and parametrically efficient manner. It improves the robustness and generalization of convolution without sacrificing the performance on common images.
• Score: 76.15687106423859
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Convolutional Neural Networks (CNNs) have exhibited their great power in a variety of vision tasks. However, the lack of transform-invariant property limits their further applications in complicated real-world scenarios. In this work, we proposed a novel generalized one dimension convolutional operator (OneDConv), which dynamically transforms the convolution kernels based on the input features in a computationally and parametrically efficient manner. The proposed operator can extract the transform-invariant features naturally. It improves the robustness and generalization of convolution without sacrificing the performance on common images. The proposed OneDConv operator can substitute the vanilla convolution, thus it can be incorporated into current popular convolutional architectures and trained end-to-end readily. On several popular benchmarks, OneDConv outperforms the original convolution operation and other proposed models both in canonical and distorted images.

Related papers

• LDConv: Linear deformable convolution for improving convolutional neural networks [18.814748446649627]
  Linear Deformable Convolution (LDConv) is a plug-and-play convolutional operation that can replace the convolutional operation to improve network performance. LDConv corrects the growth trend of the number of parameters for standard convolution and Deformable Conv to a linear growth.
  arXiv  Detail & Related papers  (2023-11-20T07:54:54Z)
• Convolutions and More as Einsum: A Tensor Network Perspective with Advances for Second-Order Methods [2.8645507575980074]
  We simplify convolutions by viewing them as tensor networks (TNs) TNs allow reasoning about the underlying tensor multiplications by drawing diagrams, manipulating them to perform function transformations like differentiation, and efficiently evaluating them with einsum. Our TN implementation accelerates KFAC variant up to 4.5x while removing the standard implementation's memory overhead, and enables new hardware-efficient dropouts for approximate backpropagation.
  arXiv  Detail & Related papers  (2023-07-05T13:19:41Z)
• 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)
• Empowering Networks With Scale and Rotation Equivariance Using A Similarity Convolution [16.853711292804476]
  We devise a method that endows CNNs with simultaneous equivariance with respect to translation, rotation, and scaling. Our approach defines a convolution-like operation and ensures equivariance based on our proposed scalable Fourier-Argand representation. We validate the efficacy of our approach in the image classification task, demonstrating its robustness and the generalization ability to both scaled and rotated inputs.
  arXiv  Detail & Related papers  (2023-03-01T08:43:05Z)
• 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)
• CSformer: Bridging Convolution and Transformer for Compressive Sensing [65.22377493627687]
  This paper proposes a hybrid framework that integrates the advantages of leveraging detailed spatial information from CNN and the global context provided by transformer for enhanced representation learning. The proposed approach is an end-to-end compressive image sensing method, composed of adaptive sampling and recovery. The experimental results demonstrate the effectiveness of the dedicated transformer-based architecture for compressive sensing.
  arXiv  Detail & Related papers  (2021-12-31T04:37:11Z)
• Dynamic Clone Transformer for Efficient Convolutional Neural Netwoks [0.0]
  We introduce a novel concept termed multi-path fully connected pattern (MPFC) to rethink the interdependencies of topology pattern, accuracy and efficiency for ConvNets. Inspired by MPFC, we propose a dual-branch module named dynamic clone transformer (DCT) where one branch generates multiple replicas from inputs and another branch reforms those clones through a series of difference vectors conditional on inputs itself to produce more variants.
  arXiv  Detail & Related papers  (2021-06-12T13:42:28Z)
• X-volution: On the unification of convolution and self-attention [52.80459687846842]
  We propose a multi-branch elementary module composed of both convolution and self-attention operation. The proposed X-volution achieves highly competitive visual understanding improvements.
  arXiv  Detail & Related papers  (2021-06-04T04:32:02Z)
• DO-Conv: Depthwise Over-parameterized Convolutional Layer [66.46704754669169]
  We propose to augment a convolutional layer with an additional depthwise convolution, where each input channel is convolved with a different 2D kernel. We show with extensive experiments that the mere replacement of conventional convolutional layers with DO-Conv layers boosts the performance of CNNs.
  arXiv  Detail & Related papers  (2020-06-22T06:57:10Z)

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.