Invariant object recognition has been one of the most rewarding are of research in computer vision as there are many applications need the capability of recognizing objects of interest in various environments. However, there is no single technique which claims to achieve the goal in all possible conditions and domains. Out of many techniques, convolutional network has proved to be a good candid...

Experimenting novel ideas on deep convolutional neural networks (DCNNs) with big datasets is hampered by the fact that network training requires huge computational resources in the terms of CPU and GPU power and hours. One option is to downscale the problem, e.g., less classes and less samples, but this is undesirable with DCNNs whose performance is largely data-dependent. In this work, we take...

In recent automatic speech recognition studies, deep learning architecture applications for acoustic modeling have eclipsed conventional sound features such as Mel-frequency cepstral coefficients. However, for visual speech recognition (VSR) studies, handcrafted visual feature extraction mechanisms are still widely utilized. In this paper, we propose to apply a convolutional neural network (CNN...

Recent developments in the field of deep learning have shown that convolutional networks with several layers can approach human level accuracy in tasks such as handwritten digit classification and object recognition. It is observed that the state-of-the-art performance is obtained from model ensembles, where several models are trained on the same data and their predictions probabilities are ave...

This paper presents the Deep Convolution Inverse Graphics Network (DC-IGN) that aims to learn an interpretable representation of images that is disentangled with respect to various transformations such as object out-of-plane rotations, lighting variations, and texture. The DC-IGN model is composed of multiple layers of convolution and de-convolution operators and is trained using the Stochastic...

In this paper, we propose a novel unsupervised deep learning model, called PCA-based Convolutional Network (PCN). The architecture of PCN is composed of several feature extraction stages and a nonlinear output stage. Particularly, each feature extraction stage includes two layers: a convolutional layer and a feature pooling layer. In the convolutional layer, the filter banks are simply learned ...

Convolutional Neural Network demonstrates high performance on ImageNet Large-Scale Visual Recognition Challenges contest. Nevertheless, the publish results only show overall performance for all images classes. There is no further analysis for what special images get worse results and how they could be improved. In this paper, we provide deep performance analysis base on different types of image...

Traditional recommendation systems rely on past usage data in order to generate new recommendations. Those approaches fail to generate sensible recommendations for new users and items into the system due to missing information about their past interactions. In this paper, we propose a solution for successfully addressing item-cold start problem which uses model-based approach and recent advance...

In this technical report, we present a bunch of methods for the task 4 of Detection and Classification of Acoustic Scenes and Events 2017 (DCASE2017) challenge. This task evaluates systems for the large-scale detection of sound events using weakly labeled training data. The data are YouTube video excerpts focusing on transportation and warnings due to their industry applications. There are two ...