Convex Hulloo: Minimally Supervised Learning with Extreme Hulloo Search – This paper addresses the problem of high-dimensional high-resolution images. In this work, we propose a new deep nonlinear generative model to learn high-dimensional shape images by considering their temporal dynamics. We train the deep model via convolutional layers for predicting the shape features of the image by minimizing the reconstruction error. Our experiments show that our model provides high-resolution shape images with a rich temporal structure and can learn accurate predictions that outperform previous methods.
We propose a novel deep learning framework for solving a multi-instance visual system task, called multi-instance object segmentation (MID). The MID framework is a joint convolutional recurrent network (CNN), a convolutional hierarchical recurrent network (CNN) and an input-to-output network (IRNN). By combining the proposed CNN and IRNN, the MID framework achieves very accurate segmentation in an unsupervised manner. Compared to previous works, these two networks are able to produce very low performance with high accuracy, even when the MID is not connected with the CNN and IRNN. In the paper, we focus on the problem of multi-instance object segmentation via IRNN and MID with a common approach to jointly training the networks. We also show the efficiency of the proposed framework to jointly improve the segmentation performance and the performance of the CNN models for the visual system segmentation task.
A Note on Support Vector Machines in Machine Learning
A survey of perceptual-motor training
Convex Hulloo: Minimally Supervised Learning with Extreme Hulloo Search
Multi-level object recognition with distributed residual descriptors
Learning Image Representation for Complex ProblemsWe propose a novel deep learning framework for solving a multi-instance visual system task, called multi-instance object segmentation (MID). The MID framework is a joint convolutional recurrent network (CNN), a convolutional hierarchical recurrent network (CNN) and an input-to-output network (IRNN). By combining the proposed CNN and IRNN, the MID framework achieves very accurate segmentation in an unsupervised manner. Compared to previous works, these two networks are able to produce very low performance with high accuracy, even when the MID is not connected with the CNN and IRNN. In the paper, we focus on the problem of multi-instance object segmentation via IRNN and MID with a common approach to jointly training the networks. We also show the efficiency of the proposed framework to jointly improve the segmentation performance and the performance of the CNN models for the visual system segmentation task.