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Found 10 papers, 2 papers with code
PSyCo: Manifold Span Reduction for Super Resolution
The main challenge in Super Resolution (SR) is to discover the mapping between the low- and high-resolution manifolds of image patches, a complex ill-posed problem which has recently been addressed through piecewise linear regression with promising results.
Hybrid Cosine Based Convolutional Neural Networks
Convolutional neural networks (CNNs) have demonstrated their capability to solve different kind of problems in a very huge number of applications.
Residual Attention Graph Convolutional Network for Geometric 3D Scene Classification
Geometric 3D scene classification is a very challenging task.
FuCiTNet: Improving the generalization of deep learning networks by the fusion of learned class-inherent transformations
It is widely known that very small datasets produce overfitting in Deep Neural Networks (DNNs), i. e., the network becomes highly biased to the data it has been trained on.
2D-3D Geometric Fusion Network using Multi-Neighbourhood Graph Convolution for RGB-D Indoor Scene Classification
Multi-modal fusion has been proved to help enhance the performance of scene classification tasks.
Channel-Wise Early Stopping without a Validation Set via NNK Polytope Interpolation
Motivated by our observations, we use CW-DeepNNK to propose a novel early stopping criterion that (i) does not require a validation set, (ii) is based on a task performance metric, and (iii) allows stopping to be reached at different points for each channel.
Channel redundancy and overlap in convolutional neural networks with channel-wise NNK graphs
Feature spaces in the deep layers of convolutional neural networks (CNNs) are often very high-dimensional and difficult to interpret.
SkinningNet: Two-Stream Graph Convolutional Neural Network for Skinning Prediction of Synthetic Characters
Whereas previous methods pre-compute handcrafted features that relate the mesh and the skeleton or assume a fixed topology of the skeleton, the proposed method extracts this information in an end-to-end learnable fashion by jointly learning the best relationship between mesh vertices and skeleton joints.
Learning task-specific features for 3D pointcloud graph creation
Processing 3D pointclouds with Deep Learning methods is not an easy task.
Study of Manifold Geometry using Multiscale Non-Negative Kernel Graphs
Modern machine learning systems are increasingly trained on large amounts of data embedded in high-dimensional spaces.
