A Feature Based Deep Learning Recognition System For Indoor Action Recognition – Deep generative models (GANs) have attracted a lot of attention in recent years due to their potential and usefulness in the field of action-adversarial learning. GANs have traditionally been implemented as generative models with a deep network architecture built over some feature vectors. In this paper, we present a new method for learning a deep generative model (GAN) for indoor action recognition when using a set of latent representations. This method is based on learning a generative model over a dataset with the goal of modeling which objects are given from the dataset. The network is trained with a fully convolutional network to represent a set of latent representations of a target object. The network then learns a deep gAN. The learned model is referred to as the Deep GAN. We demonstrate that using the deep GAN in an indoor object recognition method significantly outperforms the other state-of-the-art methods in terms of the number of labeled objects over all types of instances.
This paper presents a novel method for supervised learning for face detection. The method first learns a similarity graph from labeled face images, or RGB images. Then, we learn a similarity graph for segmentation that is based on a novel feature vector representation. After the segmentation, training for a face detection problem is formulated in terms of the discriminative similarity of images from different classes. We achieve this by leveraging recent advances in deep learning as well as the recently proposed Neural Network-Aided Perceptron (NNAP) method. This method works on both visual and physiological datasets. We show how the network can be used to successfully perform face detection in two scenarios: visual face detection and an adaptive face tracker. Our preliminary method achieves state-of-the-art accuracies of ~83% on the MNIST and ~97% on the TIMIT dataset, and shows promising results on both visual and physiological datasets.
Using the G-CNNs as Convolutional Networks: Learning to Match with Recurrent Neural Networks
A Feature Based Deep Learning Recognition System For Indoor Action Recognition
Learning Optimal Linear Regression with Periodontal Gray-scale Distributions
Spatially Aware Convolutional Neural Networks for Person Re-IdentificationThis paper presents a novel method for supervised learning for face detection. The method first learns a similarity graph from labeled face images, or RGB images. Then, we learn a similarity graph for segmentation that is based on a novel feature vector representation. After the segmentation, training for a face detection problem is formulated in terms of the discriminative similarity of images from different classes. We achieve this by leveraging recent advances in deep learning as well as the recently proposed Neural Network-Aided Perceptron (NNAP) method. This method works on both visual and physiological datasets. We show how the network can be used to successfully perform face detection in two scenarios: visual face detection and an adaptive face tracker. Our preliminary method achieves state-of-the-art accuracies of ~83% on the MNIST and ~97% on the TIMIT dataset, and shows promising results on both visual and physiological datasets.