Tiny Image Classification using Four-Block Convolutional Neural Network

Sharif, Muhammad; Kausar, Asia; Park, Jinhyuck; Shin, Dong Ryeol

doi:10.1109/ictc46691.2019.8940002

Cited by 7 publications

(6 citation statements)

References 11 publications

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“…With respect to the selected CNN-based models, Block CNN [62], ResNet [63], and DenseNet [64] have significantly deeper structures and more parameters than our model. To ensure a fair comparison and account for the small input image size of 8 × 8, we simplified these models' structures and applied fewer layers and kernels for image classification.…”

Section: Methodsmentioning

confidence: 95%

Hybrid Quantum-Classical Convolutional Neural Network Model for Image Classification

Fan,

Shi,

Guggemos

et al. 2024

IEEE Trans. Neural Netw. Learning Syst.

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Image classification plays an important role in remote sensing. Earth observation (EO) has inevitably arrived in the big data era, but the high requirement on computation power has already become a bottleneck for analyzing large amounts of remote sensing data with sophisticated machine learning models. Exploiting quantum computing might contribute to a solution to tackle this challenge by leveraging quantum properties. This article introduces a hybrid quantum-classical convolutional neural network (QC-CNN) that applies quantum computing to effectively extract high-level critical features from EO data for classification purposes. Besides that, the adoption of the amplitude encoding technique reduces the required quantum bit resources. The complexity analysis indicates that the proposed model can accelerate the convolutional operation in comparison with its classical counterpart. The model's performance is evaluated with different EO benchmarks, including Overhead-MNIST, So2Sat LCZ42, PatternNet, RSI-CB256, and NaSC-TG2, through the TensorFlow Quantum platform, and it can achieve better performance than its classical counterpart and have higher generalizability, which verifies the validity of the QC-CNN model on EO data classification tasks.

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Section: Methodsmentioning

confidence: 95%

Hybrid Quantum-Classical Convolutional Neural Network Model for Image Classification

Fan,

Shi,

Guggemos

et al. 2024

IEEE Trans. Neural Netw. Learning Syst.

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“…For that reason, some research was performed on tiny images classification. An interesting work is detailed in [17], where they present a 4Block-4CNN model that performs well on tiny images of 32 × 32 pixels from the CINIC-10 dataset. Another related work is provided in [18], where they used the Tiny ImageNet dataset with images of 64 × 64.…”

Section: Related Workmentioning

confidence: 99%

Moving Object Path Prediction in Traffic Scenes Using Contextual Information

2023

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“…Those subthemes of AI are applied to many sectors, such as health institutions, education, and management, through varying applications related to systems security. These abovementioned processes have been widely deployed to solve important security issues such as the following application trends (Figure 1): for use in robotic systems [1,13,16,22,74,75].…”

Section: Literature Trends: Ai and Systems Securitymentioning

confidence: 99%

“…First, it has been argued that it is opportune to develop a software library for creating artificial neural networks for machine learning to solve non-standard tasks [74], along a decentralized and integrated AI environment that can accommodate video data storage and event-driven video processing, gathered from varying sources, such as video surveillance systems [16], which images could be improved through AI [75].…”

Section: Neural Networkmentioning

confidence: 99%

The Impact of Artificial Intelligence on Data System Security: A Literature Review

Raimundo

Rosário

2021

Sensors

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Diverse forms of artificial intelligence (AI) are at the forefront of triggering digital security innovations based on the threats that are arising in this post-COVID world. On the one hand, companies are experiencing difficulty in dealing with security challenges with regard to a variety of issues ranging from system openness, decision making, quality control, and web domain, to mention a few. On the other hand, in the last decade, research has focused on security capabilities based on tools such as platform complacency, intelligent trees, modeling methods, and outage management systems in an effort to understand the interplay between AI and those issues. the dependence on the emergence of AI in running industries and shaping the education, transports, and health sectors is now well known in the literature. AI is increasingly employed in managing data security across economic sectors. Thus, a literature review of AI and system security within the current digital society is opportune. This paper aims at identifying research trends in the field through a systematic bibliometric literature review (LRSB) of research on AI and system security. the review entails 77 articles published in the Scopus® database, presenting up-to-date knowledge on the topic. the LRSB results were synthesized across current research subthemes. Findings are presented. the originality of the paper relies on its LRSB method, together with an extant review of articles that have not been categorized so far. Implications for future research are suggested.

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Tiny Image Classification using Four-Block Convolutional Neural Network

Cited by 7 publications

References 11 publications

Hybrid Quantum-Classical Convolutional Neural Network Model for Image Classification

Hybrid Quantum-Classical Convolutional Neural Network Model for Image Classification

Moving Object Path Prediction in Traffic Scenes Using Contextual Information

The Impact of Artificial Intelligence on Data System Security: A Literature Review

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