DeepAI AI Chat
Log In Sign Up

Deep Learning with Sets and Point Clouds

by   Siamak Ravanbakhsh, et al.
Carnegie Mellon University

We introduce a simple permutation equivariant layer for deep learning with set structure.This type of layer, obtained by parameter-sharing, has a simple implementation and linear-time complexity in the size of each set. We use deep permutation-invariant networks to perform point-could classification and MNIST-digit summation, where in both cases the output is invariant to permutations of the input. In a semi-supervised setting, where the goal is make predictions for each instance within a set, we demonstrate the usefulness of this type of layer in set-outlier detection as well as semi-supervised learning with clustering side-information.


page 6

page 11


Semi-Supervised Learning with Ladder Networks

We combine supervised learning with unsupervised learning in deep neural...

An Overview of Deep Semi-Supervised Learning

Deep neural networks demonstrated their ability to provide remarkable pe...

Safe Semi-Supervised Learning of Sum-Product Networks

In several domains obtaining class annotations is expensive while at the...

DetMatch: Two Teachers are Better Than One for Joint 2D and 3D Semi-Supervised Object Detection

While numerous 3D detection works leverage the complementary relationshi...

Geometric Algebra Attention Networks for Small Point Clouds

Much of the success of deep learning is drawn from building architecture...

Classifying Unordered Feature Sets with Convolutional Deep Averaging Networks

Unordered feature sets are a nonstandard data structure that traditional...

Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets

Permutation invariant neural networks are a promising tool for making pr...

Code Repositories


Deep neural networks for point-clouds evolution

view repo