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Semi-continuous data have a point mass at zero and are continuous with positive support. Such data arise naturally in several real-life situations like signals in a blind source separation problem, daily rainfall at a location, sales of durable goods among many others. Therefore, efficient estimation of the underlying probability density function is of significant interest.

In this paper we propose a Deep Autoencoder Mixture Clustering(DAMIC) algorithm based on a mixture of deep autoencoders whereeach cluster is represented by an autoencoder. A clustering networktransforms the data into another space and then selects one of theclusters. Next, the autoencoder associated with this cluster is usedto reconstruct the data-point. The clustering algorithm jointly learnsthe nonlinear data representation and the set of autoencoders. Theoptimal clustering is found by minimizing the reconstruction loss ofthe mixture of autoencoder network.

We present a novel variational generative adversarial network (VGAN) based on Wasserstein loss to learn a latent representation
from a face image that is invariant to identity but preserves head-pose information. This facilitates synthesis of a realistic face
image with the same head pose as a given input image, but with a different identity. One application of this network is in
privacy-sensitive scenarios; after identity replacement in an image, utility, such as head pose, can still

An omnidirectional image (ODI) enables viewers to look in every direction from a fixed point through a head-mounted display providing an immersive experience compared to that of a standard image. Designing immersive virtual reality systems with ODIs is challenging as they require high resolution content. In this paper, we study super-resolution for ODIs and propose an improved generative adversarial network based model which is optimized to handle the artifacts obtained in the spherical observational space.

The growing use of virtual autonomous agents in applications like games and entertainment demands better control policies for natural-looking movements and actions. Unlike the conventional approach of hard-coding motion routines, we propose a deep learning method for obtaining control policies by directly mimicking raw video demonstrations. Previous methods in this domain rely on extracting low-dimensional features from expert videos followed by a separate hand-crafted reward estimation step.

A novel single-image rain removal method is proposed based on multi-scale cascading image generation (MSCG). In particular, the proposed method consists of an encoder extracting multi-scale features from images and a decoder generating de-rained images with a cascading mechanism. The encoder ensembles the convolution neural networks using the kernels with different sizes, and integrates their outputs across different scales.

Convolutional neural networks (CNN) have shown state-of-the-art results for low-level computer vision problems such as stereo and monocular disparity estimations, but still, have much room to further improve their performance in terms of accuracy, numbers of parameters, etc. Recent works have uncovered the advantages of using an unsupervised scheme to train CNN’s to estimate monocular disparity, where only the relatively-easy-to-obtain stereo images are needed for training.

Portrait segmentation is becoming a hot topic nowadays.
In this paper we propose a novel framework to cope with
the high precision requirements that portrait segmentation
demands on boundary area by deep refinement of the
portrait matting. Our approach introduces three novel
techniques. First, a trimap is proposed by fusing information
coming from two well-known techniques for image
segmentation, i.e., Mask R-CNN and DensePose. Second,
an alpha matting algorithm runs over the previous trimap