RRL:Regional Rotation Layer in Convolutional Neural Networks
- URL: http://arxiv.org/abs/2202.12509v1
- Date: Fri, 25 Feb 2022 06:07:53 GMT
- Title: RRL:Regional Rotation Layer in Convolutional Neural Networks
- Authors: Zongbo Hao, Tao Zhang, Mingwang Chen, Kaixu Zhou
- Abstract summary: Convolutional Neural Networks (CNNs) perform very well in image classification and object detection.
This paper proposes a module that can be inserted into the existing networks, and directly incorporates the rotation invariance into the feature extraction layers of the CNNs.
This module does not have learnable parameters and will not increase the complexity of the model.
- Score: 2.131909135487625
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Convolutional Neural Networks (CNNs) perform very well in image
classification and object detection in recent years, but even the most advanced
models have limited rotation invariance. Known solutions include the
enhancement of training data and the increase of rotation invariance by
globally merging the rotation equivariant features. These methods either
increase the workload of training or increase the number of model parameters.
To address this problem, this paper proposes a module that can be inserted into
the existing networks, and directly incorporates the rotation invariance into
the feature extraction layers of the CNNs. This module does not have learnable
parameters and will not increase the complexity of the model. At the same time,
only by training the upright data, it can perform well on the rotated testing
set. These advantages will be suitable for fields such as biomedicine and
astronomy where it is difficult to obtain upright samples or the target has no
directionality. Evaluate our module with LeNet-5, ResNet-18 and tiny-yolov3, we
get impressive results.
Related papers
- Just How Flexible are Neural Networks in Practice? [89.80474583606242]
It is widely believed that a neural network can fit a training set containing at least as many samples as it has parameters.
In practice, however, we only find solutions via our training procedure, including the gradient and regularizers, limiting flexibility.
arXiv Detail & Related papers (2024-06-17T12:24:45Z) - Achieving Rotation Invariance in Convolution Operations: Shifting from Data-Driven to Mechanism-Assured [18.910817148765176]
This paper designs a set of new convolution operations that are natually invariant to arbitrary rotations.
We compare their performance with previous rotation-invariant convolutional neural networks (RI-CNNs)
The results show that RIConvs significantly improve the accuracy of these CNN backbones, especially when the training data is limited.
arXiv Detail & Related papers (2024-04-17T12:21:57Z) - 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) - As large as it gets: Learning infinitely large Filters via Neural Implicit Functions in the Fourier Domain [22.512062422338914]
Recent work in neural networks for image classification has seen a strong tendency towards increasing the spatial context.
We propose a module for studying the effective filter size of convolutional neural networks.
Our analysis shows that, although the proposed networks could learn very large convolution kernels, the learned filters are well localized and relatively small in practice.
arXiv Detail & Related papers (2023-07-19T14:21:11Z) - Solving Large-scale Spatial Problems with Convolutional Neural Networks [88.31876586547848]
We employ transfer learning to improve training efficiency for large-scale spatial problems.
We propose that a convolutional neural network (CNN) can be trained on small windows of signals, but evaluated on arbitrarily large signals with little to no performance degradation.
arXiv Detail & Related papers (2023-06-14T01:24:42Z) - RIC-CNN: Rotation-Invariant Coordinate Convolutional Neural Network [56.42518353373004]
We propose a new convolutional operation, called Rotation-Invariant Coordinate Convolution (RIC-C)
By replacing all standard convolutional layers in a CNN with the corresponding RIC-C, a RIC-CNN can be derived.
It can be observed that RIC-CNN achieves the state-of-the-art classification on the rotated test dataset of MNIST.
arXiv Detail & Related papers (2022-11-21T19:27:02Z) - Neural Attentive Circuits [93.95502541529115]
We introduce a general purpose, yet modular neural architecture called Neural Attentive Circuits (NACs)
NACs learn the parameterization and a sparse connectivity of neural modules without using domain knowledge.
NACs achieve an 8x speedup at inference time while losing less than 3% performance.
arXiv Detail & Related papers (2022-10-14T18:00:07Z) - Focal Sparse Convolutional Networks for 3D Object Detection [121.45950754511021]
We introduce two new modules to enhance the capability of Sparse CNNs.
They are focal sparse convolution (Focals Conv) and its multi-modal variant of focal sparse convolution with fusion.
For the first time, we show that spatially learnable sparsity in sparse convolution is essential for sophisticated 3D object detection.
arXiv Detail & Related papers (2022-04-26T17:34:10Z) - Adjoint Rigid Transform Network: Task-conditioned Alignment of 3D Shapes [86.2129580231191]
Adjoint Rigid Transform (ART) Network is a neural module which can be integrated with a variety of 3D networks.
ART learns to rotate input shapes to a learned canonical orientation, which is crucial for a lot of tasks.
We will release our code and pre-trained models for further research.
arXiv Detail & Related papers (2021-02-01T20:58:45Z) - Learning Rotation-Invariant Representations of Point Clouds Using
Aligned Edge Convolutional Neural Networks [29.3830445533532]
Point cloud analysis is an area of increasing interest due to the development of 3D sensors that are able to rapidly measure the depth of scenes accurately.
Applying deep learning techniques to perform point cloud analysis is non-trivial due to the inability of these methods to generalize to unseen rotations.
To address this limitation, one usually has to augment the training data, which can lead to extra computation and require larger model complexity.
This paper proposes a new neural network called the Aligned Edge Convolutional Neural Network (AECNN) that learns a feature representation of point clouds relative to Local Reference Frames (LRFs)
arXiv Detail & Related papers (2021-01-02T17:36:00Z) - Rotated Ring, Radial and Depth Wise Separable Radial Convolutions [13.481518628796692]
In this work, we address trainable rotation invariant convolutions and the construction of nets.
On the one hand, we show that our approach is rotationally invariant for different models and on different public data sets.
The rotationally adaptive convolution models presented are more computationally intensive than normal convolution models.
arXiv Detail & Related papers (2020-10-02T09:01:51Z)
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.