Popular density estimation methods such as Generative Adversarial Networks (GANs), and Variational Autoencoders (VAE) enforce the latent representation to follow simple distributions such as isotropic Gaussian. In this paper, we claim that learning a complicated distribution over the latent space of an auto-encoder enables accurate modeling over complicated data distributions. We propose a two ...

Popular generative model learning methods such as Generative Adversarial Networks (GANs), and Variational Autoencoders (VAE) enforce the latent representation to follow simple distributions such as isotropic Gaussian. In this paper, we argue that learning a complicated distribution over the latent space of an auto-encoder enables more accurate modeling of complicated data distributions. Based o...

there are three major strategies to form neural network ensembles. the simplest one is the cross validation strategy in which all members are trained with the same training data. bagging and boosting strategies pro-duce perturbed sample from training data. this paper provides an ideal model based on two important factors: activation function and number of neurons in the hidden layer and based u...

premature ventricular contraction (pvc) is one of the common cardiac arrhythmias. the occurrence of pvc is dangerous in people who have recently undergone heart. a pvc beat can easily be diagnosed by a doctor based on the shape of the electrocardiogram signal. but in automatic detection, extracting several important features from each beat is required. in this paper, a method for automatic dete...

precipitation is one of important parameters of climatology and atmospheric science that have more importance in human life. recently, extensive flood and drought entered many damage to most parts of the world. precipitation forecasting and alerts management role is responsible for these problems. today, artificial neural networks are one of developed method that applied for estimate and predic...

in this paper, the application of neural networks for simulation and optimization of the cogeneration systems has been presented. cgam problem, a benchmark in cogeneration systems, is chosen as a casestudy. thermodynamic model includes precise modeling of the whole plant. for simulation of the steadysate behavior, the static neural network is applied. then using dynamic neural network, plant is...

The segmentation of cloud and snow in satellite images is a key step for subsequent image analysis, interpretation, other applications. In this paper, method based on deep convolutional neural network (DCNN) with enhanced encoder–decoder architecture—ED-CNN—is proposed. method, the atrous spatial pyramid pooling (ASPP) module used to enhance encoder, while decoder fusion features from different...

Unsupervised video object segmentation is proposed in this paper, using an adaptively trained neural network structure followed by a face and body detection scheme. The latter uses probabilistic modeling for applying the face and body detection task. The algorithm is incorporated along with a rate control mechanism, which allocates more bits to regions of importance, such as humans in video con...

The comparison of heterogeneous samples extensively exists in many applications, especially in the task of image classification. In this paper, we propose a simple but effective coupled neural network, called Deeply Coupled Autoencoder Networks (DCAN), which seeks to build two deep neural networks, coupled with each other in every corresponding layers. In DCAN, each deep structure is developed ...

Fig. 1 provides an easy-to-understand design of ReViSE. In all of our experiments, GoogLeNet is pre-trained on ImageNet [2] images. Without fine-tuning, we directly extract the top layer activations (1024-dim) as our input image features followed by a common log(1+v) pre-processing step. For the textual attributes, we pre-process them through a standard l2 normalization. In ReViSE, we set α = 1...