3D Point Capsule Networks

In this paper, we propose 3D point-capsule networks, an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data. 3D capsule networks arise as a direct consequence of our novel unified 3D auto-encoder formulation. Their dynamic routing scheme and the peculiar 2D latent space deployed by our approach bring in improvements for several common point cloud-related tasks, such as object classification, object reconstruction and part segmentation as substantiated by our extensive evaluations. Moreover, it enables new applications such as part interpolation and replacement.Comment: As published in CVPR 2019 (camera ready version), with supplementary materia

3D Point Capsule Networks

In this paper, we propose 3D point-capsule networks, an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data. 3D capsule networks arise as a direct consequence of our novel unified 3D auto-encoder formulation. Their dynamic routing scheme and the peculiar 2D latent space deployed by our approach bring in improvements for several common point cloud-related tasks, such as object classification, object reconstruction and part segmentation as substantiated by our extensive evaluations. Moreover, it enables new applications such as part interpolation and replacement

AUTOMATIC IDENTIFICATION OF ANIMALS IN THE WILD: A COMPARATIVE STUDY BETWEEN C-CAPSULE NETWORKS AND DEEP CONVOLUTIONAL NEURAL NETWORKS.

The evolution of machine learning and computer vision in technology has driven a lot of improvements and innovation into several domains. We see it being applied for credit decisions, insurance quotes, malware detection, fraud detection, email composition, and any other area having enough information to allow the machine to learn patterns. Over the years the number of sensors, cameras, and cognitive pieces of equipment placed in the wilderness has been growing exponentially. However, the resources (human) to leverage these data into something meaningful are not improving at the same rate. For instance, a team of scientist volunteers took 8.4 years, 17000 hours at a rate of 40 hours/week to label 3.2 million images from the Serengeti wild park. For our research, we are going to focus on wild data and keep proving that deep learning can do better and faster than the human equivalent labor for the same task. Moreover, this is also an opportunity to present some custom Capsule Networks architectures to the deep learning community while solving the above-mentioned critical problem. Incidentally, we are going to take advantage of these data to make a comparative study on multiple deep learning models, specifically, VGG-net, RES-net, and a custom made Convolutional-Capsule Network. We benchmark our work with the Serengeti project where Mohammed Sadegh et al. recently published a 92% top-1 accuracy [23] and Gomez et al. had a 58% top-1 accuracy [12]. We successfully reached 96.4% top-1 accuracy on the same identification task. Concurrently, we reached up to 79.48% top-1 testing accuracy 33on a big, complex dataset using capsule network, which out-performed the best results of Capsule networks on a complex dataset from Edgar Xi et al. with 71% testing accuracy [8,33,27]

Homogeneous Vector Capsules Enable Adaptive Gradient Descent in Convolutional Neural Networks

Capsules are the name given by Geoffrey Hinton to vector-valued neurons. Neural networks traditionally produce a scalar value for an activated neuron. Capsules, on the other hand, produce a vector of values, which Hinton argues correspond to a single, composite feature wherein the values of the components of the vectors indicate properties of the feature such as transformation or contrast. We present a new way of parameterizing and training capsules that we refer to as homogeneous vector capsules (HVCs). We demonstrate, experimentally, that altering a convolutional neural network (CNN) to use HVCs can achieve superior classification accuracy without increasing the number of parameters or operations in its architecture as compared to a CNN using a single final fully connected layer. Additionally, the introduction of HVCs enables the use of adaptive gradient descent, reducing the dependence a model's achievable accuracy has on the finely tuned hyperparameters of a non-adaptive optimizer. We demonstrate our method and results using two neural network architectures. First, a very simple monolithic CNN that when using HVCs achieved a 63% improvement in top-1 classification accuracy and a 35% improvement in top-5 classification accuracy over the baseline architecture. Second, with the CNN architecture referred to as Inception v3 that achieved similar accuracies both with and without HVCs. Additionally, the simple monolithic CNN when using HVCs showed no overfitting after more than 300 epochs whereas the baseline showed overfitting after 30 epochs. We use the ImageNet ILSVRC 2012 classification challenge dataset with both networks.https://arxiv.org/abs/1906.08676v