# Graph-based Isometry Invariant Representation Learning

@article{Khasanova2017GraphbasedII, title={Graph-based Isometry Invariant Representation Learning}, author={Renata Khasanova and Pascal Frossard}, journal={ArXiv}, year={2017}, volume={abs/1703.00356} }

Learning transformation invariant representations of visual data is an important problem in computer vision. Deep convolutional networks have demonstrated remarkable results for image and video classification tasks. However, they have achieved only limited success in the classification of images that undergo geometric transformations. In this work we present a novel Transformation Invariant Graph-based Network (TIGraNet), which learns graph-based features that are inherently invariant to…

## 48 Citations

### Isometric Transformation Invariant Graph-based Deep Neural Network

- Computer ScienceArXiv
- 2018

A novel Transformation Invariant Graph-based Network (TIGraNet), which learns graph-based features that are inherently invariant to isometric transformations such as rotation and translation of input images.

### GIFT: Learning Transformation-Invariant Dense Visual Descriptors via Group CNNs

- Computer ScienceNeurIPS
- 2019

A novel visual descriptor named Group Invariant Feature Transform (GIFT) is introduced, which is both discriminative and robust to geometric transformations and outperforms state-of-the-art methods on several benchmark datasets and practically improves the performance of relative pose estimation.

### Equivariance-bridged SO(2)-Invariant Representation Learning using Graph Convolutional Network

- Computer Science, MathematicsBMVC
- 2021

The proposed deep equivariance-bridged SO(2) invariant network achieves the state-of-the-art image classiﬁcation performance on rotated MNIST and CIFAR-10 images, where the models are trained with a non-augmented dataset only.

### Image Classification with Hierarchical Multigraph Networks

- Computer ScienceBMVC
- 2019

This work shows best practices for designing GCNs for image classification; in some cases even outperforming CNNs on the MNIST, CIFAR-10 and PASCAL image datasets.

### Non-Parametric Transformation Networks for Learning General Invariances from Data

- Computer ScienceAAAI
- 2019

This paper introduces a new class of deep convolutional architectures called Non-Parametric Transformation Networks (NPTNs) which can learn general invariances and symmetries directly from data and replaces ConvNets with NPTNs within Capsule Networks and shows that this enables Capsule Nets to perform even better.

### Non-Parametric Transformation Networks

- Computer ScienceAAAI 2019
- 2018

A new class of convolutional architectures called Non-Parametric Transformation Networks (NPTNs) which can learn general invariances and symmetries directly from data directly using gradient descent is introduced.

### Graph-Based Classification of Omnidirectional Images

- Computer Science2017 IEEE International Conference on Computer Vision Workshops (ICCVW)
- 2017

This paper proposes a principled way of graph construction such that convolutional filters respond similarly for the same pattern on different positions of the image regardless of lens distortions, and shows that the proposed method outperforms current techniques for the omnidirectional image classification problem.

### Improving Spectral Graph Convolution for Learning Graph-level Representation

- Computer ScienceArXiv
- 2021

This work serves as a spatial understanding that quantitatively measures the effects of the spectrum to input signals in comparison to the well-known spectral understanding as high/low-pass filters and sheds the light on developing powerful graph representation models.

### Learning Non-Parametric Invariances from Data with Permanent Random Connectomes

- Computer ScienceBMVC
- 2020

A new architectural layer for convolutional networks which is capable of learning general invariances from data itself, and interestingly, motivates and incorporates permanent random connectomes, thereby being called Permanent Random Connectome Non-Parametric Transformation Networks (PRC-NPTN).

### Graph Pooling with Node Proximity for Hierarchical Representation Learning

- Computer ScienceArXiv
- 2020

A novel graph pooling strategy that leverages node proximity to improve the hierarchical representation learning of graph data with their multi-hop topology with the combination of the affine transformation and kernel trick using the Gaussian RBF function.

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