High-Quality Image Compressed Sensing and Reconstruction with Multi-scale Dilated Convolutional Neural Network

Intelligent learning diagnosis is a critical engine of intelligent tutoring systems, which aims to estimate learners’ current knowledge mastery status and predict their future learning performance. The significant challenge with traditional learning diagnosis methods is the inability to balance diagnostic accuracy and interpretability. Although the existing psychometric-based learning diagnosis methods provide some domain interpretation through cognitive parameters, they have insufficient modeling capability with a shallow structure for large-scale learning data. While the deep learning-based learning diagnosis methods have improved the accuracy of learning performance prediction, their inherent black-box properties lead to a lack of interpretability, making their results untrustworthy for educational applications. To settle the abovementioned problem, the proposed unified interpretable intelligent learning diagnosis framework, which benefits from the powerful representation learning ability of deep learning and the interpretability of psychometrics, achieves a better performance of learning prediction and provides interpretability from three aspects: cognitive parameters, learner-resource response network, and weights of self-attention mechanism. Within the proposed framework, this paper presents a two-channel learning diagnosis mechanism LDM-ID as well as a three-channel learning diagnosis mechanism LDM-HMI. Experiments on two real-world datasets and a simulation dataset show that our method has higher accuracy in predicting learners’ performances compared with the state-of-the-art models and can provide valuable educational interpretability for applications such as precise learning resource recommendation and personalized learning tutoring in intelligent tutoring systems.

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“…and have been widely used in computer vision [28,29], speech processing [30,31], natural language understanding [32], and other felds [33].…”

Section: Learning Diagnosis Based On Deep Learning Deep Learning Meth...mentioning

confidence: 99%

“…j • Value i(25) similarity(Query, Key i ) � Query•Key i (26) end for(27) end for(28) f p � MaxPool(Relu(Conv(f a ))) (29) p � σ(W 3 × f p + b 3 )(30) return p, EC, SC, attention weights ALGORITHM 2: Tree-channel learning diagnosis mechanism based on the fusion of Ho-DINA, MIRT, and IRT.…”

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confidence: 99%

A Unified Interpretable Intelligent Learning Diagnosis Framework for Learning Performance Prediction in Intelligent Tutoring Systems

Wang

Yan

Zeng

et al. 2023

International Journal of Intelligent Systems

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“…Images are ubiquitous in the era of big data, and image processing technology has been developed and applied in many fields [1]. However, in the fields of medical imaging, remote sensing, or others, there are some restrictions when obtaining images, for example, characterizing information by fewer data [2], massively parallel processing [3], abundant redundant information in cloud storage [4], less bandwidth of information transmission [5], and space required for signal storage [6], etc.…”

Section: Introductionmentioning

confidence: 99%

“…CS theory, which obtains sampling information far below the frequency of Nyquist sampling and recovers the original signal from the sampling information with high probability, breaks through the limitation of the traditional sampling law [1,8]. CS theory has a profound impact on statistics, information theory, coding theory, etc.…”

Section: Introductionmentioning

confidence: 99%

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JSRNN: Joint Sampling and Reconstruction Neural Networks for High Quality Image Compressed Sensing

Zeng¹,

Ye²,

Wang³

et al. 2022

Preprint

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Most Deep Learning (DL) based Compressed Sensing (DCS) algorithms adopt a single neural network for signal reconstruction, and fail to jointly consider the influences of the sampling operation for reconstruction. In this paper, we propose unified framework, which jointly considers the sampling and reconstruction process for image compressive sensing based on welldesigned cascade neural networks. Two sub-networks, which are the sampling sub-network and the reconstruction sub-network, are included in the proposed framework. In the sampling sub-network, an adaptive full connected layer instead of the traditional random matrix is used to mimic the sampling operator. In the reconstruction sub-network, a cascade network combining stacked denoising autoencoder (SDA) and convolutional neural network (CNN) is designed to reconstruct signals. The SDA is used to solve the signal mapping problem and the signals are initially reconstructed. Furthermore, CNN is used to fully recover the structure and texture features of the image to obtain better reconstruction performance. Extensive experiments show that this framework outperforms many other state-ofthe-art methods, especially at low sampling rates.

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Deep Ensemble Feature Extraction Based Classification of Bleeding Regions Using Wireless Capsule Endoscopy Images

Bandopadhyay,

Roy,

Chaudhuri

et al. 2024

Intelligent Data Analytics for Bioinformatics and Biomedical Systems

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High-Quality Image Compressed Sensing and Reconstruction with Multi-scale Dilated Convolutional Neural Network

Cited by 39 publications

References 36 publications

A Unified Interpretable Intelligent Learning Diagnosis Framework for Learning Performance Prediction in Intelligent Tutoring Systems

A Unified Interpretable Intelligent Learning Diagnosis Framework for Learning Performance Prediction in Intelligent Tutoring Systems

JSRNN: Joint Sampling and Reconstruction Neural Networks for High Quality Image Compressed Sensing

Deep Ensemble Feature Extraction Based Classification of Bleeding Regions Using Wireless Capsule Endoscopy Images

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