14 research outputs found
A Deep Wavelet AutoEncoder Scheme for Image Compression
For many years and since its appearance, Digital Wavelet Transform DWT has been used with great success in a wide range of applications especially in image compression and signal de-noising. Combined with several and various approaches, this powerful mathematical tool has shown its strength to compress images with high compression ratio and good visual quality. This paper attempts to demonstrate that it is needless to follow the classical three stages process of compression: pixels transformation, quantization and binary coding when compressing images using the baseline method. Indeed, in this work, we propose a new scheme of image compression system based on an unsupervised convolutional neural network AutoEncoder (CAE) that will reconstruct the approximate sub-band issue from image decomposition by the wavelet transform DWT. In order To evaluate the model’s performance we use Kodak dataset containing a set of 24 images never compressed with a lossy algorithm technique and applied the approach on every one of them. We compared our achieved results with those obtained using standard compression method. We draw this comparison in terms of four performance parameters: Structural Similarity Index Metrix SSIM, Peak Signal to Noise Ratio PSNR, Mean Square Error MSE and Compression Ratio CR. The proposed scheme offers significate improvement in distortion metrics over the traditional image compression method when evaluated for perceptual quality moreover it produces better visual quality images with clearer details and textures which demonstrates its effectiveness and its robustness
On quadrature rules for solving Partial Differential Equations using Neural Networks
Neural Networks have been widely used to solve Partial Differential Equations. These methods require to approximate definite integrals using quadrature rules. Here, we illustrate via 1D numerical examples the quadrature problems that may arise in these applications and propose several alternatives to overcome them, namely: Monte Carlo methods, adaptive integration, polynomial approximations of the Neural Network output, and the inclusion of regularization terms in the loss. We also discuss the advantages and limitations of each proposed numerical integration scheme. We advocate the use of Monte Carlo methods for high dimensions (above 3 or 4), and adaptive integration or polynomial approximations for low dimensions (3 or below). The use of regularization terms is a mathematically elegant alternative that is valid for any spatial dimension; however, it requires certain regularity assumptions on the solution and complex mathematical analysis when dealing with sophisticated Neural Networks
To Compress or Not to Compress -- Self-Supervised Learning and Information Theory: A Review
Deep neural networks have demonstrated remarkable performance in supervised
learning tasks but require large amounts of labeled data. Self-supervised
learning offers an alternative paradigm, enabling the model to learn from data
without explicit labels. Information theory has been instrumental in
understanding and optimizing deep neural networks. Specifically, the
information bottleneck principle has been applied to optimize the trade-off
between compression and relevant information preservation in supervised
settings. However, the optimal information objective in self-supervised
learning remains unclear. In this paper, we review various approaches to
self-supervised learning from an information-theoretic standpoint and present a
unified framework that formalizes the \textit{self-supervised
information-theoretic learning problem}. We integrate existing research into a
coherent framework, examine recent self-supervised methods, and identify
research opportunities and challenges. Moreover, we discuss empirical
measurement of information-theoretic quantities and their estimators. This
paper offers a comprehensive review of the intersection between information
theory, self-supervised learning, and deep neural networks
Research on prognostic risk assessment model for acute ischemic stroke based on imaging and multidimensional data
Accurately assessing the prognostic outcomes of patients with acute ischemic stroke and adjusting treatment plans in a timely manner for those with poor prognosis is crucial for intervening in modifiable risk factors. However, there is still controversy regarding the correlation between imaging-based predictions of complications in acute ischemic stroke. To address this, we developed a cross-modal attention module for integrating multidimensional data, including clinical information, imaging features, treatment plans, prognosis, and complications, to achieve complementary advantages. The fused features preserve magnetic resonance imaging (MRI) characteristics while supplementing clinical relevant information, providing a more comprehensive and informative basis for clinical diagnosis and treatment. The proposed framework based on multidimensional data for activity of daily living (ADL) scoring in patients with acute ischemic stroke demonstrates higher accuracy compared to other state-of-the-art network models, and ablation experiments confirm the effectiveness of each module in the framework
DeepGD: A Multi-Objective Black-Box Test Selection Approach for Deep Neural Networks
Deep neural networks (DNNs) are widely used in various application domains
such as image processing, speech recognition, and natural language processing.
However, testing DNN models may be challenging due to the complexity and size
of their input domain. Particularly, testing DNN models often requires
generating or exploring large unlabeled datasets. In practice, DNN test
oracles, which identify the correct outputs for inputs, often require expensive
manual effort to label test data, possibly involving multiple experts to ensure
labeling correctness. In this paper, we propose DeepGD, a black-box
multi-objective test selection approach for DNN models. It reduces the cost of
labeling by prioritizing the selection of test inputs with high fault revealing
power from large unlabeled datasets. DeepGD not only selects test inputs with
high uncertainty scores to trigger as many mispredicted inputs as possible but
also maximizes the probability of revealing distinct faults in the DNN model by
selecting diverse mispredicted inputs. The experimental results conducted on
four widely used datasets and five DNN models show that in terms of
fault-revealing ability: (1) White-box, coverage-based approaches fare poorly,
(2) DeepGD outperforms existing black-box test selection approaches in terms of
fault detection, and (3) DeepGD also leads to better guidance for DNN model
retraining when using selected inputs to augment the training set
Number Systems for Deep Neural Network Architectures: A Survey
Deep neural networks (DNNs) have become an enabling component for a myriad of
artificial intelligence applications. DNNs have shown sometimes superior
performance, even compared to humans, in cases such as self-driving, health
applications, etc. Because of their computational complexity, deploying DNNs in
resource-constrained devices still faces many challenges related to computing
complexity, energy efficiency, latency, and cost. To this end, several research
directions are being pursued by both academia and industry to accelerate and
efficiently implement DNNs. One important direction is determining the
appropriate data representation for the massive amount of data involved in DNN
processing. Using conventional number systems has been found to be sub-optimal
for DNNs. Alternatively, a great body of research focuses on exploring suitable
number systems. This article aims to provide a comprehensive survey and
discussion about alternative number systems for more efficient representations
of DNN data. Various number systems (conventional/unconventional) exploited for
DNNs are discussed. The impact of these number systems on the performance and
hardware design of DNNs is considered. In addition, this paper highlights the
challenges associated with each number system and various solutions that are
proposed for addressing them. The reader will be able to understand the
importance of an efficient number system for DNN, learn about the widely used
number systems for DNN, understand the trade-offs between various number
systems, and consider various design aspects that affect the impact of number
systems on DNN performance. In addition, the recent trends and related research
opportunities will be highlightedComment: 28 page