Face Attribute Modification Using Fine-Tuned Attribute-Modification Network

Islam, Naeem Ul; Park, Jaebyung

doi:10.3390/electronics9050743

Cited by 4 publications

(3 citation statements)

References 27 publications

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“…Regression-based approaches [1,3,21,32] translate images from one domain to another, and play a key role in estimating depth maps from RGB images. Considering regression-based approaches, [21] recently proposed BA-DualAE, which comprises two auto-encoders whose individual latent spaces are associated with a bidirectional regression network.…”

Section: Literature Reviewmentioning

confidence: 99%

Analyzing the Effectiveness of Classification and Regression for Depth Estimation of Highly Dynamic Terrain

Islam¹,

Park

2022

Preprint

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Depth estimation is considered a problem of translating RGB images to their corresponding depth maps. It plays an essential role in robotics and autonomous navigation as it allows for an understanding of the nature of the terrain. Owing to its natural importance, considerable attention has been paid by the research community in the past decade to depth estimations, including studies focusing on environmental condition-, classification-, or regression-based approaches. Among them, classification- and regression-based approaches are considered the key candidates for depth estimations, but it is not clear which approach performs better under what conditions. To investigate and define a clear method for providing an accurate depth estimation from a single RGB image, this study extensively evaluates these approaches qualitatively and quantitatively by considering a highly dynamic dataset. Based on extensive qualitative and quantitative experimental analyses, this study provides possible future directions for accurate depth estimation from a single RGB image.

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Section: Literature Reviewmentioning

confidence: 99%

Analyzing the Effectiveness of Classification and Regression for Depth Estimation of Highly Dynamic Terrain

Islam¹,

Park

2022

Preprint

Self Cite

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“…However, this approach requires the availability of the entire training set at the test time. Image-to-image translations [28][29][30][31][32], which translate images from one domain to another, play a key role in estimating depth from RGB images. Considering image-toimage translation, the authors in [29] recently proposed BA-DualAE, which is composed of two auto-encoders, where the latent spaces of the different domains are linked with a bidirectional regression network.…”

Section: Related Workmentioning

confidence: 99%

Depth Estimation From a Single RGB Image Using Fine-Tuned Generative Adversarial Network

Islam

Park

2021

IEEE Access

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Estimating the depth map from a single RGB image is important to understand the nature of the terrain in robot navigation and has attracted considerable attention in the past decade. The existing approaches can accurately estimate the depth from a single RGB image, considering a highly structured environment. The problem becomes more challenging when the terrain is highly dynamic. We propose a finetuned generative adversarial network to estimate the depth map effectively for a given single RGB image. The proposed network is composed of a fine-tuned generator and a global discriminator. The encoder part of the generator takes input RGB images and depth maps and generates their joint distribution in the latent space. Subsequently, the decoder part of the generator decodes the depth map from the joint distribution. The discriminator takes real and fake pairs in three different configurations and then guides the generator to estimate the depth map from the given RGB image accordingly. Finally, we conducted extensive experiments with a highly dynamic environment dataset for verifying the effectiveness and feasibility of the proposed approach. The proposed approach could decode the depth map from the joint distribution more effectively and accurately than the existing approaches.

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“…However, these large-scale experimental screening techniques for the identification of ubiquitination sites are time consuming, expensive, and laborious. Owing to the advantages and emergence of machine learning models, they have been utilized in different fields, such as natural language processing (NLP) [ 28 , 29 ], energy load forecasting [ 30 ], speech recognition [ 31 ], image recognition [ 32 , 33 , 34 ], and computational biology [ 35 , 36 , 37 , 38 ]. Computational predictors were built to predict ubiquitination sites in a cost- and time-effective manner.…”

Section: Introductionmentioning

confidence: 99%

UbiComb: A Hybrid Deep Learning Model for Predicting Plant-Specific Protein Ubiquitylation Sites

Siraj

Lim

Tayara

et al. 2021

Genes

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Protein ubiquitylation is an essential post-translational modification process that performs a critical role in a wide range of biological functions, even a degenerative role in certain diseases, and is consequently used as a promising target for the treatment of various diseases. Owing to the significant role of protein ubiquitylation, these sites can be identified by enzymatic approaches, mass spectrometry analysis, and combinations of multidimensional liquid chromatography and tandem mass spectrometry. However, these large-scale experimental screening techniques are time consuming, expensive, and laborious. To overcome the drawbacks of experimental methods, machine learning and deep learning-based predictors were considered for prediction in a timely and cost-effective manner. In the literature, several computational predictors have been published across species; however, predictors are species-specific because of the unclear patterns in different species. In this study, we proposed a novel approach for predicting plant ubiquitylation sites using a hybrid deep learning model by utilizing convolutional neural network and long short-term memory. The proposed method uses the actual protein sequence and physicochemical properties as inputs to the model and provides more robust predictions. The proposed predictor achieved the best result with accuracy values of 80% and 81% and F-scores of 79% and 82% on the 10-fold cross-validation and an independent dataset, respectively. Moreover, we also compared the testing of the independent dataset with popular ubiquitylation predictors; the results demonstrate that our model significantly outperforms the other methods in prediction classification results.

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Face Attribute Modification Using Fine-Tuned Attribute-Modification Network

Cited by 4 publications

References 27 publications

Analyzing the Effectiveness of Classification and Regression for Depth Estimation of Highly Dynamic Terrain

Analyzing the Effectiveness of Classification and Regression for Depth Estimation of Highly Dynamic Terrain

Depth Estimation From a Single RGB Image Using Fine-Tuned Generative Adversarial Network

UbiComb: A Hybrid Deep Learning Model for Predicting Plant-Specific Protein Ubiquitylation Sites

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