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Buch ; Online: Face to Cartoon Incremental Super-Resolution using Knowledge Distillation

Devkatte, Trinetra / Dubey, Shiv Ram / Singh, Satish Kumar / Hadid, Abdenour

2024

Abstract: Facial super-resolution/hallucination is an important area of research that seeks to enhance low-resolution facial images for a variety of applications. While Generative Adversarial Networks (GANs) have shown promise in this area, their ability to adapt ... ...

Abstract	Facial super-resolution/hallucination is an important area of research that seeks to enhance low-resolution facial images for a variety of applications. While Generative Adversarial Networks (GANs) have shown promise in this area, their ability to adapt to new, unseen data remains a challenge. This paper addresses this problem by proposing an incremental super-resolution using GANs with knowledge distillation (ISR-KD) for face to cartoon. Previous research in this area has not investigated incremental learning, which is critical for real-world applications where new data is continually being generated. The proposed ISR-KD aims to develop a novel unified framework for facial super-resolution that can handle different settings, including different types of faces such as cartoon face and various levels of detail. To achieve this, a GAN-based super-resolution network was pre-trained on the CelebA dataset and then incrementally trained on the iCartoonFace dataset, using knowledge distillation to retain performance on the CelebA test set while improving the performance on iCartoonFace test set. Our experiments demonstrate the effectiveness of knowledge distillation in incrementally adding capability to the model for cartoon face super-resolution while retaining the learned knowledge for facial hallucination tasks in GANs.
Schlagwörter	Computer Science - Computer Vision and Pattern Recognition ; Electrical Engineering and Systems Science - Image and Video Processing
Thema/Rubrik (Code)	006
Erscheinungsdatum	2024-01-27
Erscheinungsland	us
Dokumenttyp	Buch ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Artikel ; Online: Vehicular Environment Identification Based on Channel State Information and Deep Learning.

Ribouh, Soheyb / Sadli, Rahmad / Elhillali, Yassin / Rivenq, Atika / Hadid, Abdenour

Sensors (Basel, Switzerland)

2022 Band 22, Heft 22

Abstract: This paper presents a novel vehicular environment identification approach based on deep learning. It consists of exploiting the vehicular wireless channel characteristics in the form of Channel State Information (CSI) in the receiver side of a connected ... ...

Abstract	This paper presents a novel vehicular environment identification approach based on deep learning. It consists of exploiting the vehicular wireless channel characteristics in the form of Channel State Information (CSI) in the receiver side of a connected vehicle in order to identify the environment type in which the vehicle is driving, without any need to implement specific sensors such as cameras or radars. We consider environment identification as a classification problem, and propose a new convolutional neural network (CNN) architecture to deal with it. The estimated CSI is used as the input feature to train the model. To perform the identification process, the model is targeted for implementation in an autonomous vehicle connected to a vehicular network (VN). The proposed model is extensively evaluated, showing that it can reliably recognize the surrounding environment with high accuracy (96.48%). Our model is compared to related approaches and state-of-the-art classification architectures. The experiments show that our proposed model yields favorable performance compared to all other considered methods.
Mesh-Begriff(e)	Deep Learning ; Neural Networks, Computer
Sprache	Englisch
Erscheinungsdatum	2022-11-21
Erscheinungsland	Switzerland
Dokumenttyp	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s22229018
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Buch ; Online: Pain Analysis using Adaptive Hierarchical Spatiotemporal Dynamic Imaging

Serraoui, Issam / Granger, Eric / Hadid, Abdenour / Taleb-Ahmed, Abdelmalik

2023

Abstract: Automatic pain intensity estimation plays a pivotal role in healthcare and medical fields. While many methods have been developed to gauge human pain using behavioral or physiological indicators, facial expressions have emerged as a prominent tool for ... ...

Abstract	Automatic pain intensity estimation plays a pivotal role in healthcare and medical fields. While many methods have been developed to gauge human pain using behavioral or physiological indicators, facial expressions have emerged as a prominent tool for this purpose. Nevertheless, the dependence on labeled data for these techniques often renders them expensive and time-consuming. To tackle this, we introduce the Adaptive Hierarchical Spatio-temporal Dynamic Image (AHDI) technique. AHDI encodes spatiotemporal changes in facial videos into a singular RGB image, permitting the application of simpler 2D deep models for video representation. Within this framework, we employ a residual network to derive generalized facial representations. These representations are optimized for two tasks: estimating pain intensity and differentiating between genuine and simulated pain expressions. For the former, a regression model is trained using the extracted representations, while for the latter, a binary classifier identifies genuine versus feigned pain displays. Testing our method on two widely-used pain datasets, we observed encouraging results for both tasks. On the UNBC database, we achieved an MSE of 0.27 outperforming the SOTA which had an MSE of 0.40. On the BioVid dataset, our model achieved an accuracy of 89.76%, which is an improvement of 5.37% over the SOTA accuracy. Most notably, for distinguishing genuine from simulated pain, our accuracy stands at 94.03%, marking a substantial improvement of 8.98%. Our methodology not only minimizes the need for extensive labeled data but also augments the precision of pain evaluations, facilitating superior pain management.
Schlagwörter	Computer Science - Computer Vision and Pattern Recognition
Thema/Rubrik (Code)	006
Erscheinungsdatum	2023-12-11
Erscheinungsland	us
Dokumenttyp	Buch ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Buch ; Online: Skew Probabilistic Neural Networks for Learning from Imbalanced Data

Naik, Shraddha M. / Chakraborty, Tanujit / Hadid, Abdenour / Chakraborty, Bibhas

2023

Abstract: Real-world datasets often exhibit imbalanced data distribution, where certain class levels are severely underrepresented. In such cases, traditional pattern classifiers have shown a bias towards the majority class, impeding accurate predictions for the ... ...

Abstract	Real-world datasets often exhibit imbalanced data distribution, where certain class levels are severely underrepresented. In such cases, traditional pattern classifiers have shown a bias towards the majority class, impeding accurate predictions for the minority class. This paper introduces an imbalanced data-oriented approach using probabilistic neural networks (PNNs) with a skew normal probability kernel to address this major challenge. PNNs are known for providing probabilistic outputs, enabling quantification of prediction confidence and uncertainty handling. By leveraging the skew normal distribution, which offers increased flexibility, particularly for imbalanced and non-symmetric data, our proposed Skew Probabilistic Neural Networks (SkewPNNs) can better represent underlying class densities. To optimize the performance of the proposed approach on imbalanced datasets, hyperparameter fine-tuning is imperative. To this end, we employ a population-based heuristic algorithm, Bat optimization algorithms, for effectively exploring the hyperparameter space. We also prove the statistical consistency of the density estimates which suggests that the true distribution will be approached smoothly as the sample size increases. Experimental simulations have been conducted on different synthetic datasets, comparing various benchmark-imbalanced learners. Our real-data analysis shows that SkewPNNs substantially outperform state-of-the-art machine learning methods for both balanced and imbalanced datasets in most experimental settings.
Schlagwörter	Statistics - Machine Learning ; Computer Science - Machine Learning
Thema/Rubrik (Code)	006
Erscheinungsdatum	2023-12-10
Erscheinungsland	us
Dokumenttyp	Buch ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Buch ; Online: Knowledge-based Deep Learning for Modeling Chaotic Systems

Elabid, Zakaria / Chakraborty, Tanujit / Hadid, Abdenour

2022

Abstract: Deep Learning has received increased attention due to its unbeatable success in many fields, such as computer vision, natural language processing, recommendation systems, and most recently in simulating multiphysics problems and predicting nonlinear ... ...

Abstract	Deep Learning has received increased attention due to its unbeatable success in many fields, such as computer vision, natural language processing, recommendation systems, and most recently in simulating multiphysics problems and predicting nonlinear dynamical systems. However, modeling and forecasting the dynamics of chaotic systems remains an open research problem since training deep learning models requires big data, which is not always available in many cases. Such deep learners can be trained from additional information obtained from simulated results and by enforcing the physical laws of the chaotic systems. This paper considers extreme events and their dynamics and proposes elegant models based on deep neural networks, called knowledge-based deep learning (KDL). Our proposed KDL can learn the complex patterns governing chaotic systems by jointly training on real and simulated data directly from the dynamics and their differential equations. This knowledge is transferred to model and forecast real-world chaotic events exhibiting extreme behavior. We validate the efficiency of our model by assessing it on three real-world benchmark datasets: El Nino sea surface temperature, San Juan Dengue viral infection, and Bj{\o}rn{\o}ya daily precipitation, all governed by extreme events' dynamics. Using prior knowledge of extreme events and physics-based loss functions to lead the neural network learning, we ensure physically consistent, generalizable, and accurate forecasting, even in a small data regime.
Schlagwörter	Computer Science - Machine Learning ; Physics - Computational Physics ; Statistics - Machine Learning
Thema/Rubrik (Code)	006
Erscheinungsdatum	2022-09-09
Erscheinungsland	us
Dokumenttyp	Buch ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Buch ; Online: W-Transformers

Sasal, Lena / Chakraborty, Tanujit / Hadid, Abdenour

A Wavelet-based Transformer Framework for Univariate Time Series Forecasting

2022

Abstract: Deep learning utilizing transformers has recently achieved a lot of success in many vital areas such as natural language processing, computer vision, anomaly detection, and recommendation systems, among many others. Among several merits of transformers, ... ...

Abstract	Deep learning utilizing transformers has recently achieved a lot of success in many vital areas such as natural language processing, computer vision, anomaly detection, and recommendation systems, among many others. Among several merits of transformers, the ability to capture long-range temporal dependencies and interactions is desirable for time series forecasting, leading to its progress in various time series applications. In this paper, we build a transformer model for non-stationary time series. The problem is challenging yet crucially important. We present a novel framework for univariate time series representation learning based on the wavelet-based transformer encoder architecture and call it W-Transformer. The proposed W-Transformers utilize a maximal overlap discrete wavelet transformation (MODWT) to the time series data and build local transformers on the decomposed datasets to vividly capture the nonstationarity and long-range nonlinear dependencies in the time series. Evaluating our framework on several publicly available benchmark time series datasets from various domains and with diverse characteristics, we demonstrate that it performs, on average, significantly better than the baseline forecasters for short-term and long-term forecasting, even for datasets that consist of only a few hundred training samples.
Schlagwörter	Computer Science - Machine Learning ; Economics - Econometrics ; Electrical Engineering and Systems Science - Signal Processing ; Statistics - Machine Learning
Thema/Rubrik (Code)	006
Erscheinungsdatum	2022-09-08
Erscheinungsland	us
Dokumenttyp	Buch ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Buch ; Online: Probabilistic AutoRegressive Neural Networks for Accurate Long-range Forecasting

Panja, Madhurima / Chakraborty, Tanujit / Kumar, Uttam / Hadid, Abdenour

2022

Abstract: Forecasting time series data is a critical area of research with applications spanning from stock prices to early epidemic prediction. While numerous statistical and machine learning methods have been proposed, real-life prediction problems often require ...

Abstract	Forecasting time series data is a critical area of research with applications spanning from stock prices to early epidemic prediction. While numerous statistical and machine learning methods have been proposed, real-life prediction problems often require hybrid solutions that bridge classical forecasting approaches and modern neural network models. In this study, we introduce the Probabilistic AutoRegressive Neural Networks (PARNN), capable of handling complex time series data exhibiting non-stationarity, nonlinearity, non-seasonality, long-range dependence, and chaotic patterns. PARNN is constructed by improving autoregressive neural networks (ARNN) using autoregressive integrated moving average (ARIMA) feedback error, combining the explainability, scalability, and "white-box-like" prediction behavior of both models. Notably, the PARNN model provides uncertainty quantification through prediction intervals, setting it apart from advanced deep learning tools. Through comprehensive computational experiments, we evaluate the performance of PARNN against standard statistical, machine learning, and deep learning models, including Transformers, NBeats, and DeepAR. Diverse real-world datasets from macroeconomics, tourism, epidemiology, and other domains are employed for short-term, medium-term, and long-term forecasting evaluations. Our results demonstrate the superiority of PARNN across various forecast horizons, surpassing the state-of-the-art forecasters. The proposed PARNN model offers a valuable hybrid solution for accurate long-range forecasting. By effectively capturing the complexities present in time series data, it outperforms existing methods in terms of accuracy and reliability. The ability to quantify uncertainty through prediction intervals further enhances the model's usefulness in decision-making processes.
Schlagwörter	Statistics - Machine Learning ; Computer Science - Machine Learning
Thema/Rubrik (Code)	006
Erscheinungsdatum	2022-04-01
Erscheinungsland	us
Dokumenttyp	Buch ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Artikel ; Online: Map-Matching-Based Localization Using Camera and Low-Cost GPS for Lane-Level Accuracy.

Sadli, Rahmad / Afkir, Mohamed / Hadid, Abdenour / Rivenq, Atika / Taleb-Ahmed, Abdelmalik

Sensors (Basel, Switzerland)

2022 Band 22, Heft 7

Abstract: For self-driving systems or autonomous vehicles (AVs), accurate lane-level localization is a important for performing complex driving maneuvers. Classical GNSS-based methods are usually not accurate enough to have lane-level localization to support the ... ...

Abstract	For self-driving systems or autonomous vehicles (AVs), accurate lane-level localization is a important for performing complex driving maneuvers. Classical GNSS-based methods are usually not accurate enough to have lane-level localization to support the AV's maneuvers. LiDAR-based localization can provide accurate localization. However, the price of LiDARs is still one of the big issues preventing this kind of solution from becoming wide-spread commodity. Therefore, in this work, we propose a low-cost solution for lane-level localization using a vision-based system and a low-cost GPS to achieve high precision lane-level localization. Experiments in real-world and real-time demonstrate that the proposed method achieves good lane-level localization accuracy, outperforming solutions based on only GPS.
Sprache	Englisch
Erscheinungsdatum	2022-03-22
Erscheinungsland	Switzerland
Dokumenttyp	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s22072434
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification.

Paladini, Emanuela / Vantaggiato, Edoardo / Bougourzi, Fares / Distante, Cosimo / Hadid, Abdenour / Taleb-Ahmed, Abdelmalik

Journal of imaging

2021 Band 7, Heft 3

Abstract: In recent years, automatic tissue phenotyping has attracted increasing interest in the Digital Pathology (DP) field. For Colorectal Cancer (CRC), tissue phenotyping can diagnose the cancer and differentiate between different cancer grades. The ... ...

Abstract	In recent years, automatic tissue phenotyping has attracted increasing interest in the Digital Pathology (DP) field. For Colorectal Cancer (CRC), tissue phenotyping can diagnose the cancer and differentiate between different cancer grades. The development of Whole Slide Images (WSIs) has provided the required data for creating automatic tissue phenotyping systems. In this paper, we study different hand-crafted feature-based and deep learning methods using two popular multi-classes CRC-tissue-type databases: Kather-CRC-2016 and CRC-TP. For the hand-crafted features, we use two texture descriptors (LPQ and BSIF) and their combination. In addition, two classifiers are used (SVM and NN) to classify the texture features into distinct CRC tissue types. For the deep learning methods, we evaluate four Convolutional Neural Network (CNN) architectures (ResNet-101, ResNeXt-50, Inception-v3, and DenseNet-161). Moreover, we propose two Ensemble CNN approaches: Mean-Ensemble-CNN and NN-Ensemble-CNN. The experimental results show that the proposed approaches outperformed the hand-crafted feature-based methods, CNN architectures and the state-of-the-art methods in both databases.
Sprache	Englisch
Erscheinungsdatum	2021-03-09
Erscheinungsland	Switzerland
Dokumenttyp	Journal Article
ZDB-ID	2824270-1
ISSN	2313-433X ; 2313-433X
ISSN (online)	2313-433X
ISSN	2313-433X
DOI	10.3390/jimaging7030051
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Per-COVID-19: A Benchmark Dataset for COVID-19 Percentage Estimation from CT-Scans.

Bougourzi, Fares / Distante, Cosimo / Ouafi, Abdelkrim / Dornaika, Fadi / Hadid, Abdenour / Taleb-Ahmed, Abdelmalik

Journal of imaging

2021 Band 7, Heft 9

Abstract: COVID-19 infection recognition is a very important step in the fight against the COVID-19 pandemic. In fact, many methods have been used to recognize COVID-19 infection including Reverse Transcription Polymerase Chain Reaction (RT-PCR), X-ray scan, and ... ...

Abstract	COVID-19 infection recognition is a very important step in the fight against the COVID-19 pandemic. In fact, many methods have been used to recognize COVID-19 infection including Reverse Transcription Polymerase Chain Reaction (RT-PCR), X-ray scan, and Computed Tomography scan (CT- scan). In addition to the recognition of the COVID-19 infection, CT scans can provide more important information about the evolution of this disease and its severity. With the extensive number of COVID-19 infections, estimating the COVID-19 percentage can help the intensive care to free up the resuscitation beds for the critical cases and follow other protocol for less severity cases. In this paper, we introduce COVID-19 percentage estimation dataset from CT-scans, where the labeling process was accomplished by two expert radiologists. Moreover, we evaluate the performance of three Convolutional Neural Network (CNN) architectures: ResneXt-50, Densenet-161, and Inception-v3. For the three CNN architectures, we use two loss functions: MSE and Dynamic Huber. In addition, two pretrained scenarios are investigated (ImageNet pretrained models and pretrained models using X-ray data). The evaluated approaches achieved promising results on the estimation of COVID-19 infection. Inception-v3 using Dynamic Huber loss function and pretrained models using X-ray data achieved the best performance for slice-level results: 0.9365, 5.10, and 9.25 for Pearson Correlation coefficient (PC), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE), respectively. On the other hand, the same approach achieved 0.9603, 4.01, and 6.79 for PCsubj, MAEsubj, and RMSEsubj, respectively, for subject-level results. These results prove that using CNN architectures can provide accurate and fast solution to estimate the COVID-19 infection percentage for monitoring the evolution of the patient state.
Sprache	Englisch
Erscheinungsdatum	2021-09-18
Erscheinungsland	Switzerland
Dokumenttyp	Journal Article
ZDB-ID	2824270-1
ISSN	2313-433X ; 2313-433X
ISSN (online)	2313-433X
ISSN	2313-433X
DOI	10.3390/jimaging7090189
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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