Tracking translation invariance in CNNs

• URL: http://arxiv.org/abs/2104.05997v1
• Date: Tue, 13 Apr 2021 08:05:56 GMT
• Title: Tracking translation invariance in CNNs
• Authors: Johannes C.Myburgh, Coenraad Mouton, Marelie H.Davel
• Abstract summary: We investigate the effect of different architectural components of a standard CNN on that network's sensitivity to translation. By varying convolutional kernel sizes and amounts of zero padding, we control the size of the feature maps produced. We also measure translation invariance at different locations within the CNN to determine the extent to which convolutional and fully connected layers, respectively, contribute to the translation invariance of a CNN as a whole.
• Score: 2.4213989921339847
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although Convolutional Neural Networks (CNNs) are widely used, their translation invariance (ability to deal with translated inputs) is still subject to some controversy. We explore this question using translation-sensitivity maps to quantify how sensitive a standard CNN is to a translated input. We propose the use of Cosine Similarity as sensitivity metric over Euclidean Distance, and discuss the importance of restricting the dimensionality of either of these metrics when comparing architectures. Our main focus is to investigate the effect of different architectural components of a standard CNN on that network's sensitivity to translation. By varying convolutional kernel sizes and amounts of zero padding, we control the size of the feature maps produced, allowing us to quantify the extent to which these elements influence translation invariance. We also measure translation invariance at different locations within the CNN to determine the extent to which convolutional and fully connected layers, respectively, contribute to the translation invariance of a CNN as a whole. Our analysis indicates that both convolutional kernel size and feature map size have a systematic influence on translation invariance. We also see that convolutional layers contribute less than expected to translation invariance, when not specifically forced to do so.

Related papers

• Metric as Transform: Exploring beyond Affine Transform for Interpretable Neural Network [2.7195102129095003]
  We find dot product neurons with global influence less interpretable as compared to local influence of euclidean distance. We develop an interpretable local dictionary based Neural Networks and use it to understand and reject adversarial examples.
  arXiv  Detail & Related papers  (2024-10-21T16:22:19Z)
• Revisiting Data Augmentation for Rotational Invariance in Convolutional Neural Networks [0.29127054707887967]
  We investigate how best to include rotational invariance in a CNN for image classification. Our experiments show that networks trained with data augmentation alone can classify rotated images nearly as well as in the normal unrotated case.
  arXiv  Detail & Related papers  (2023-10-12T15:53:24Z)
• Studying inductive biases in image classification task [0.0]
  Self-attention (SA) structures have locally independent filters and can use large kernels, which contradicts the previously popular convolutional neural networks (CNNs) We show that context awareness was the crucial property; however, large local information was not necessary to construct CA parameters.
  arXiv  Detail & Related papers  (2022-10-31T08:43:26Z)
• The Lie Derivative for Measuring Learned Equivariance [84.29366874540217]
  We study the equivariance properties of hundreds of pretrained models, spanning CNNs, transformers, and Mixer architectures. We find that many violations of equivariance can be linked to spatial aliasing in ubiquitous network layers, such as pointwise non-linearities. For example, transformers can be more equivariant than convolutional neural networks after training.
  arXiv  Detail & Related papers  (2022-10-06T15:20:55Z)
• What Can Be Learnt With Wide Convolutional Neural Networks? [69.55323565255631]
  We study infinitely-wide deep CNNs in the kernel regime. We prove that deep CNNs adapt to the spatial scale of the target function. We conclude by computing the generalisation error of a deep CNN trained on the output of another deep CNN.
  arXiv  Detail & Related papers  (2022-08-01T17:19:32Z)
• Group Equivariant Subsampling [60.53371517247382]
  Subsampling is used in convolutional neural networks (CNNs) in the form of pooling or strided convolutions. We first introduce translation equivariant subsampling/upsampling layers that can be used to construct exact translation equivariant CNNs. We then generalise these layers beyond translations to general groups, thus proposing group equivariant subsampling/upsampling.
  arXiv  Detail & Related papers  (2021-06-10T16:14:00Z)
• Stride and Translation Invariance in CNNs [2.4213989921339847]
  We show that stride can greatly benefit translation invariance given that it is combined with sufficient similarity between neighbouring pixels. We also explore the efficacy of other solutions proposed, namely global average pooling, anti-aliasing, and data augmentation.
  arXiv  Detail & Related papers  (2021-03-18T09:17:06Z)
• What Does CNN Shift Invariance Look Like? A Visualization Study [87.79405274610681]
  Feature extraction with convolutional neural networks (CNNs) is a popular method to represent images for machine learning tasks. We focus on measuring and visualizing the shift invariance of extracted features from popular off-the-shelf CNN models. We conclude that features extracted from popular networks are not globally invariant, and that biases and artifacts exist within this variance.
  arXiv  Detail & Related papers  (2020-11-09T01:16:30Z)
• Learning Translation Invariance in CNNs [1.52292571922932]
  We show how, even though CNNs are not 'architecturally invariant' to translation, they can indeed 'learn' to be invariant to translation. We investigated how this pretraining affected the internal network representations. These experiments show how pretraining a network on an environment with the right 'latent' characteristics can result in the network learning deep perceptual rules.
  arXiv  Detail & Related papers  (2020-11-06T09:39:27Z)
• 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)
• ACDC: Weight Sharing in Atom-Coefficient Decomposed Convolution [57.635467829558664]
  We introduce a structural regularization across convolutional kernels in a CNN. We show that CNNs now maintain performance with dramatic reduction in parameters and computations.
  arXiv  Detail & Related papers  (2020-09-04T20:41:47Z)

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.