Deep learning based active learning technique for data annotation and improve the overall performance of classification models

Amin, Sareer Ul; Hussain, Adnan; Kim, Bumsoo; Seo, Sanghyun

doi:10.1016/j.eswa.2023.120391

Cited by 13 publications

(2 citation statements)

References 21 publications

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“…The utilization of remote-sensing (RS) imagery for land-cover (LC) classification is of paramount importance across various domains, encompassing environmental protection, agriculture and urban planning, and land resource management [1]. Recent accessibility to high-resolution remote-sensing (HRRS) images and the ability to gather multi-temporal and multi-source RS images from diverse geographic regions [2] present new opportunities for multiple-time-scale LC classification. Nevertheless, the complex features visible in HRRS images, such as geometrical and object structures, introduce new challenges for classification [3].…”

Section: Introductionmentioning

confidence: 99%

Land-Cover Classification Using Deep Learning with High-Resolution Remote-Sensing Imagery

Fayaz,

Nam,

Dang

et al. 2024

Applied Sciences

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Land-area classification (LAC) research offers a promising avenue to address the intricacies of urban planning, agricultural zoning, and environmental monitoring, with a specific focus on urban areas and their complex land usage patterns. The potential of LAC research is significantly propelled by advancements in high-resolution satellite imagery and machine learning strategies, particularly the use of convolutional neural networks (CNNs). Accurate LAC is paramount for informed urban development and effective land management. Traditional remote-sensing methods encounter limitations in precisely classifying dynamic and complex urban land areas. Therefore, in this study, we investigated the application of transfer learning with Inception-v3 and DenseNet121 architectures to establish a reliable LAC system for identifying urban land use classes. Leveraging transfer learning with these models provided distinct advantages, as it allows the LAC system to benefit from pre-trained features on large datasets, enhancing model generalization and performance compared to starting from scratch. Transfer learning also facilitates the effective utilization of limited labeled data for fine-tuning, making it a valuable strategy for optimizing model accuracy in complex urban land classification tasks. Moreover, we strategically employ fine-tuned versions of Inception-v3 and DenseNet121 networks, emphasizing the transformative impact of these architectures. The fine-tuning process enables the model to leverage pre-existing knowledge from extensive datasets, enhancing its adaptability to the intricacies of LC classification. By aligning with these advanced techniques, our research not only contributes to the evolution of remote-sensing methodologies but also underscores the paramount importance of incorporating cutting-edge methodologies, such as fine-tuning and the use of specific network architectures, in the continual enhancement of LC classification systems. Through experiments conducted on the UC-Merced_LandUse dataset, we demonstrate the effectiveness of our approach, achieving remarkable results, including 92% accuracy, 93% recall, 92% precision, and a 92% F1-score. Moreover, employing heatmap analysis further elucidates the decision-making process of the models, providing insights into the classification mechanism. The successful application of CNNs in LAC, coupled with heatmap analysis, opens promising avenues for enhanced urban planning, agricultural zoning, and environmental monitoring through more accurate and automated land-area classification.

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Section: Introductionmentioning

confidence: 99%

Land-Cover Classification Using Deep Learning with High-Resolution Remote-Sensing Imagery

Fayaz,

Nam,

Dang

et al. 2024

Applied Sciences

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“…One way to overcome this problem is to use active learning methods. Active learning is a technique to sample from unlabeled data and choose new samples to annotate and add to the training set based on a certain algorithm in order to maximize the model’s improvement [30, 31]. There are several algorithms (acquisition functions) that are often used in active learning.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Ligand-Based Virtual Screening with 3D Shape Similarity via a Distance-Aware Transformer Model

Sellner,

Mahmoud,

Lill

2023

Preprint

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Following the assumption that chemically similar molecules exhibit similar biologcial properties, ligand-based virtual screening can be a valuable starting point in drug discovery projects. While 2D-based similarity metrics generally focus on similar scaffolds or substructures, 3D-based methods can capture the shape of a molecule, allowing for the identification of compounds with different scaffolds. We recently published a proof-of-concept study which demonstrated how a Transformer model can be adapted to preserve 2D similarities in latent space in the form of Euclidean distances. In this work, we extend this research and prove that the approach can be adapted to 3D similarities. We use pharmacophore-based shape similarity as 3D similarity measure. We show that the model is able to enrich the predicted most similar hits with compounds with different scaffolds that are indeed similar in 3D space. Whereas classical pharmacophore- or shape-based 3D similarity methods rely on expensive alignment processes, in our approach, we identify similar compounds directly by the Euclidean distances in latent space. This enables for the first time the 3D screening of ultra-large databases with high efficiency.

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Dynamic Learning for Improving Anomalous Event Prediction in Surveillance Videos

Divya,

Mirnalinee,

Bhuvana

2024

Arab J Sci Eng

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Deep learning based active learning technique for data annotation and improve the overall performance of classification models

Cited by 13 publications

References 21 publications

Land-Cover Classification Using Deep Learning with High-Resolution Remote-Sensing Imagery

Land-Cover Classification Using Deep Learning with High-Resolution Remote-Sensing Imagery

Enhancing Ligand-Based Virtual Screening with 3D Shape Similarity via a Distance-Aware Transformer Model

Dynamic Learning for Improving Anomalous Event Prediction in Surveillance Videos

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