PAGER: Progressive Attribute-Guided Extendable Robust Image Generation

Azizi, Zohreh; Kuo, C.-C. Jay

doi:10.1561/116.00000034

Cited by 4 publications

(1 citation statement)

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“…image generation [16][17][18], blind image quality assessment [19], disease classification [20], face gender classification [21], and object tracking [22][23][24].…”

Section: (I)mentioning

confidence: 99%

Methodology of soft partition for image classification

Mishra,

Kuo

2024

Disruptive Technologies in Information Sciences VIII

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The idea of subspace learning machine (SLM) has been a powerful tool for machine learning (ML), and it has been successfully applied to the task of image classification. Recently, a novel SLM method was proposed, which (i) projects high-dimensional feature vectors into a 1D feature subspace, and (ii) partitions it into two disjoint sets. SLM with soft partitioning (SLM/SP) extends this approach by learning an adaptive soft decision tree (SDT) structure using local greedy subspace partitioning. After meeting the stopping criteria for all child nodes and determining the tree structure, it updates all projection vectors (PVs) globally. It enables efficient training, high classification accuracy, and a small model size. It is applied to experimental data to show its performance as a lightweight and high-performance classification method.

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“…image generation [16][17][18], blind image quality assessment [19], disease classification [20], face gender classification [21], and object tracking [22][23][24].…”

Section: (I)mentioning

confidence: 99%

Methodology of soft partition for image classification

Mishra,

Kuo

2024

Disruptive Technologies in Information Sciences VIII

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Enhancing Edge Intelligence with Highly Discriminant LNT Features

Wang,

Mishra,

Jay Kuo

2023

2023 IEEE International Conference on Big Data (BigData)

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An Overview on Generative AI at Scale With Edge–Cloud Computing

Wang,

Xue,

Wei

et al. 2023

IEEE Open J. Commun. Soc.

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As a specific category of artificial intelligence (AI), generative artificial intelligence (GenAI) generates new content that resembles what humans create. The rapid development of GenAI systems has created a huge amount of new data on the Internet, posing new challenges to current computing and communication frameworks. Currently, GenAI services rely on the traditional cloud computing framework due to the need for large computation resources. However, such services will encounter high latency because of data transmission and a high volume of user requests. On the other hand, edge-cloud computing can provide adequate computation power and low latency at the same time through the collaboration between edges and the cloud. Thus, it is attractive to build GenAI systems at scale by leveraging the edge-cloud computing paradigm. In this overview paper, we review recent developments in GenAI and edge-cloud computing, respectively. Then, we use two exemplary GenAI applications to discuss technical challenges in scaling up their solutions using edge-cloud collaborative systems. Finally, we list design considerations for training and deploying GenAI systems at scale and point out future research directions.

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PAGER: Progressive Attribute-Guided Extendable Robust Image Generation

Cited by 4 publications

References 0 publications

Methodology of soft partition for image classification

Methodology of soft partition for image classification

Enhancing Edge Intelligence with Highly Discriminant LNT Features

An Overview on Generative AI at Scale With Edge–Cloud Computing

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