We propose a Multimodal Stacked Denoising Autoencoder for learning a joint model of data that consists of multiple modalities. The model is used to extract a joint representation that fuses modalities together. We have found that this representation is useful for classification tasks. Our model is made up of layers of denoising autoencoders which are trained locally to denoise corrupted version...

Transfer learning provides an approach to solve target tasks more quickly and effectively by using previouslyacquired knowledge learned from source tasks. Most of transfer learning approaches extract knowledge of source domain in the given feature space. The issue is that single perspective can‟t mine the relationship of source domain and target domain fully. To deal with this issue, this paper...

Sentiment relevance (SR) aims at identifying content that does not contribute to sentiment analysis. Previously, automatic SR classification has been studied in a limited scope, using a single domain and feature augmentation techniques that require large hand-crafted databases. In this paper, we present experiments on SR classification with automatically learned feature representations on multi...

We address a challenging fine-grain classification problem: recognizing a font style from an image of text. In this task, it is very easy to generate lots of rendered font examples but very hard to obtain real-world labeled images. This realto-synthetic domain gap caused poor generalization to new real data in previous methods (Chen et al. (2014)). In this paper, we refer to Convolutional Neura...

The exponential increase in new coronavirus disease 2019 ({COVID-19}) cases and deaths has made COVID-19 the leading cause of death many countries. Thus, this study, we propose an efficient technique for automatic detection pneumonia based on X-ray images. A stacked denoising convolutional autoencoder (SDCA) model was proposed to classify images into three classes: normal, pneumonia, {COVID-19}...

Pretraining is widely used in deep neutral network and one of the most famous pretraining models is Deep Belief Network (DBN). The optimization formulas are different during the pretraining process for different pretraining models. In this paper, we pretrained deep neutral network by different pretraining models and hence investigated the difference between DBN and Stacked Denoising Autoencoder...

Future considerations would include ne tuning the latent SVM used to train the DPM, using stacked autoencoders to learn more complex feature representations, and optimize runtime of training algorithm to allow for larger training sets. Acknowledgements Special thanks to Professor Andrew Ng and Adam Coates for the advice they provided through the course of this project. Given its performance in ...

(Aim) COVID-19 is an ongoing infectious disease. It has caused more than 107.45 m confirmed cases and 2.35 deaths till 11/Feb/2021. Traditional computer vision methods have achieved promising results on the automatic smart diagnosis. (Method) This study aims to propose a novel deep learning method that can obtain better performance. We use pseudo-Zernike moment (PZM), derived from...

Orthogonal frequency division multiplexing is one of the efficient and flexible modulation techniques, which considered as central part many wired wireless standards. (OFDM) multiple-input multiple-output (MIMO) achieves maximum spectral efficiency data rates for mobile communication systems. Though it offers better quality services, high peak-to-average power ratio (PAPR) major issue that need...