Improving Shift Invariance in Convolutional Neural Networks with Translation Invariant Polyphase Sampling

• URL: http://arxiv.org/abs/2404.07410v1
• Date: Thu, 11 Apr 2024 00:49:38 GMT
• Title: Improving Shift Invariance in Convolutional Neural Networks with Translation Invariant Polyphase Sampling
• Authors: Sourajit Saha, Tejas Gokhale,
• Abstract summary: Downsampling operators break the shift invariance of convolutional neural networks (CNNs) We propose a learnable pooling operator called Translation Invariant Polyphase Sampling (TIPS) TIPS results in consistent performance gains in terms of accuracy, shift consistency, and shift fidelity.
• Score: 14.731788603429774
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Downsampling operators break the shift invariance of convolutional neural networks (CNNs) and this affects the robustness of features learned by CNNs when dealing with even small pixel-level shift. Through a large-scale correlation analysis framework, we study shift invariance of CNNs by inspecting existing downsampling operators in terms of their maximum-sampling bias (MSB), and find that MSB is negatively correlated with shift invariance. Based on this crucial insight, we propose a learnable pooling operator called Translation Invariant Polyphase Sampling (TIPS) and two regularizations on the intermediate feature maps of TIPS to reduce MSB and learn translation-invariant representations. TIPS can be integrated into any CNN and can be trained end-to-end with marginal computational overhead. Our experiments demonstrate that TIPS results in consistent performance gains in terms of accuracy, shift consistency, and shift fidelity on multiple benchmarks for image classification and semantic segmentation compared to previous methods and also leads to improvements in adversarial and distributional robustness. TIPS results in the lowest MSB compared to all previous methods, thus explaining our strong empirical results.

Related papers

• PseudoNeg-MAE: Self-Supervised Point Cloud Learning using Conditional Pseudo-Negative Embeddings [55.55445978692678]
  PseudoNeg-MAE is a self-supervised learning framework that enhances global feature representation of point cloud mask autoencoders. We show that PseudoNeg-MAE achieves state-of-the-art performance on the ModelNet40 and ScanObjectNN datasets.
  arXiv  Detail & Related papers  (2024-09-24T07:57:21Z)
• A Probabilistic Approach to Learning the Degree of Equivariance in Steerable CNNs [5.141137421503899]
  Steerable convolutional neural networks (SCNNs) enhance task performance by modelling geometric symmetries. Yet, unknown or varying symmetries can lead to overconstrained weights and decreased performance. This paper introduces a probabilistic method to learn the degree of equivariance in SCNNs.
  arXiv  Detail & Related papers  (2024-06-06T10:45:19Z)
• Investigating Shift Equivalence of Convolutional Neural Networks in Industrial Defect Segmentation [3.843350895842836]
  In industrial defect segmentation tasks, output consistency (also referred to equivalence) of the model is often overlooked. A novel pair of down/upsampling layers called component attention polyphase sampling (CAPS) is proposed as a replacement for the conventional sampling layers in CNNs. The experimental results on the micro surface defect (MSD) dataset and four real-world industrial defect datasets demonstrate that the proposed method exhibits higher equivalence and segmentation performance.
  arXiv  Detail & Related papers  (2023-09-29T00:04:47Z)
• SO(2) and O(2) Equivariance in Image Recognition with Bessel-Convolutional Neural Networks [63.24965775030674]
  This work presents the development of Bessel-convolutional neural networks (B-CNNs) B-CNNs exploit a particular decomposition based on Bessel functions to modify the key operation between images and filters. Study is carried out to assess the performances of B-CNNs compared to other methods.
  arXiv  Detail & Related papers  (2023-04-18T18:06:35Z)
• 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)
• 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)
• Implicit Equivariance in Convolutional Networks [1.911678487931003]
  Implicitly Equivariant Networks (IEN) induce equivariant in the different layers of a standard CNN model. We show IEN outperforms the state-of-the-art rotation equivariant tracking method while providing faster inference speed.
  arXiv  Detail & Related papers  (2021-11-28T14:44:17Z)
• Truly shift-equivariant convolutional neural networks with adaptive polyphase upsampling [28.153820129486025]
  In image classification, adaptive polyphase downsampling (APS-D) was recently proposed to make CNNs perfectly shift invariant. We propose adaptive polyphase upsampling (APS-U), a non-linear extension of conventional upsampling, which allows CNNs to exhibit perfect shift equivariance.
  arXiv  Detail & Related papers  (2021-05-09T22:33:53Z)
• Sampling-free Variational Inference for Neural Networks with Multiplicative Activation Noise [51.080620762639434]
  We propose a more efficient parameterization of the posterior approximation for sampling-free variational inference. Our approach yields competitive results for standard regression problems and scales well to large-scale image classification tasks.
  arXiv  Detail & Related papers  (2021-03-15T16:16:18Z)
• Truly shift-invariant convolutional neural networks [0.0]
  Recent works have shown that the output of a CNN can change significantly with small shifts in input. We propose adaptive polyphase sampling (APS), a simple sub-sampling scheme that allows convolutional neural networks to achieve 100% consistency in classification performance under shifts.
  arXiv  Detail & Related papers  (2020-11-28T20:57:35Z)
• Learning Invariances in Neural Networks [51.20867785006147]
  We show how to parameterize a distribution over augmentations and optimize the training loss simultaneously with respect to the network parameters and augmentation parameters. We can recover the correct set and extent of invariances on image classification, regression, segmentation, and molecular property prediction from a large space of augmentations.
  arXiv  Detail & Related papers  (2020-10-22T17:18:48Z)

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.