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Full-Text Articles in Physical Sciences and Mathematics
A Federated Deep Autoencoder For Detecting Iot Cyber Attacks, Christopher M. Regan
A Federated Deep Autoencoder For Detecting Iot Cyber Attacks, Christopher M. Regan
Master of Science in Computer Science Theses
Internet of Things (IoT) devices are mass-produced and rapidly released to the public in a rough state. IoT devices are produced by various companies satisfying various goals, such as monitoring the environment, senor trigger cameras, on-demand electrical switches. These IoT devices are produced by companies to meet a market demand quickly, producing a rough software solution that customers or other enterprises willingly buy with the expectation they will have software updates after production. These IoT devices are often heterogeneous in nature, only to receive updates at infrequently intervals, and can remain out of sight on a home or office network …
Application Of Autoencoders For Latent Pattern Analysis In Image Time Series, Jiena He
Application Of Autoencoders For Latent Pattern Analysis In Image Time Series, Jiena He
International Development, Community and Environment (IDCE)
The Earth system is considered to possess certain modes - preferred patterns of variability that can represent the latent structure of the climate system, also known as teleconnections. There are approaches to discover these patterns, Principal Components Analysis and Empirical Orthogonal Teleconnection (EOT) analysis. However, while the latter is very effective, it is computationally intensive. An autoencoder is an unsupervised neural network that learns an efficient neural representation of input data. It is considered as a dimensionality reduction tool that is highly similar to PCA and EOT. The hidden layer of an autoencoder represents the most significant information of the …
Model-Based Deep Siamese Autoencoder For Clustering Single Cell Rna-Seq Data, Zixia Meng
Model-Based Deep Siamese Autoencoder For Clustering Single Cell Rna-Seq Data, Zixia Meng
Theses
In the biological field, the smallest unit of organisms in most biological systems is the single cell, and the classification of cells is an everlasting problem. A central task for analysis of single-cell RNA-seq data is to identify and characterize novel cell types. Currently, there are several classical methods, such as K-means algorithm, spectral clustering, and Gaussian Mixture Models (GMMs), which are widely used to cluster the cells. Furthermore, typical dimensional reduction methods such as PCA, t-SNE, and ZIDA have been introduced to overcome “the curse of dimensionality”. A more recent method scDeepCluster has demonstrated improved and promising performances in …
Content Based Image Retrieval (Cbir) For Brand Logos, Enjal Parajuli
Content Based Image Retrieval (Cbir) For Brand Logos, Enjal Parajuli
Boise State University Theses and Dissertations
This thesis explores the problem of automatically detecting the presence of logos in general images. Brand logos carry the goodwill of a company and are considered to be of high value in the corporate world, and thus automatically determining whether or not a logo is present in an image can be of interest for companies that wish to protect their brand. The problem of automated logo detection is inherently complex, but is further complicated through intentional obfuscation of logo images, for example by color shifting or other slight image modifications that leave the logo intact and easily recognizable by a …
Representation Learning With Adversarial Latent Autoencoders, Stanislav Pidhorskyi M.S.
Representation Learning With Adversarial Latent Autoencoders, Stanislav Pidhorskyi M.S.
Graduate Theses, Dissertations, and Problem Reports
A large number of deep learning methods applied to computer vision problems require encoder-decoder maps. These methods include, but are not limited to, self-representation learning, generalization, few-shot learning, and novelty detection. Encoder-decoder maps are also useful for photo manipulation, photo editing, superresolution, etc. Encoder-decoder maps are typically learned using autoencoder networks.
Traditionally, autoencoder reciprocity is achieved in the image-space using pixel-wise
similarity loss, which has a widely known flaw of producing non-realistic reconstructions. This flaw is typical for the Variational Autoencoder (VAE) family and is not only limited to pixel-wise similarity losses, but is common to all methods relying upon …