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1 – 2 of 2Huaxiang Song, Chai Wei and Zhou Yong
The paper aims to tackle the classification of Remote Sensing Images (RSIs), which presents a significant challenge for computer algorithms due to the inherent characteristics of…
Abstract
Purpose
The paper aims to tackle the classification of Remote Sensing Images (RSIs), which presents a significant challenge for computer algorithms due to the inherent characteristics of clustered ground objects and noisy backgrounds. Recent research typically leverages larger volume models to achieve advanced performance. However, the operating environments of remote sensing commonly cannot provide unconstrained computational and storage resources. It requires lightweight algorithms with exceptional generalization capabilities.
Design/methodology/approach
This study introduces an efficient knowledge distillation (KD) method to build a lightweight yet precise convolutional neural network (CNN) classifier. This method also aims to substantially decrease the training time expenses commonly linked with traditional KD techniques. This approach entails extensive alterations to both the model training framework and the distillation process, each tailored to the unique characteristics of RSIs. In particular, this study establishes a robust ensemble teacher by independently training two CNN models using a customized, efficient training algorithm. Following this, this study modifies a KD loss function to mitigate the suppression of non-target category predictions, which are essential for capturing the inter- and intra-similarity of RSIs.
Findings
This study validated the student model, termed KD-enhanced network (KDE-Net), obtained through the KD process on three benchmark RSI data sets. The KDE-Net surpasses 42 other state-of-the-art methods in the literature published from 2020 to 2023. Compared to the top-ranked method’s performance on the challenging NWPU45 data set, KDE-Net demonstrated a noticeable 0.4% increase in overall accuracy with a significant 88% reduction in parameters. Meanwhile, this study’s reformed KD framework significantly enhances the knowledge transfer speed by at least three times.
Originality/value
This study illustrates that the logit-based KD technique can effectively develop lightweight CNN classifiers for RSI classification without substantial sacrifices in computation and storage costs. Compared to neural architecture search or other methods aiming to provide lightweight solutions, this study’s KDE-Net, based on the inherent characteristics of RSIs, is currently more efficient in constructing accurate yet lightweight classifiers for RSI classification.
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Dave Valliere and Charlene L. Nicholls-Nixon
Although business incubators are a widely recognized form of entrepreneurial support, this paper aims to challenge the assumption that incubation is necessarily beneficial for…
Abstract
Purpose
Although business incubators are a widely recognized form of entrepreneurial support, this paper aims to challenge the assumption that incubation is necessarily beneficial for early-stage entrepreneurs, and considers cases where, due to variability in the motives and behaviours of entrepreneurs, incubation may be unwarranted or even undesireable.
Design/methodology/approach
This study presents a theoretically derived typology of incubated entrepreneurs, based on their entrepreneurial competence and capacity for learning, which asserts that incubation may be unwarranted or even undesireable for three of the four proposed entrepreneur types. Qualitative data from interviews with entrepreneurs and managing directors from 10 business incubators is used to illustrate the existence of these types.
Findings
The data provides evidence of entrepreneurial types whose incubation may be counterproductive to the goals and objectives of their host incubators.
Practical implications
Implications for incubator management (intake screening and ongoing monitoring of portfolio) are developed and aimed at improving the outcomes of business incubation for stakeholders.
Originality/value
The paper contributes to the incubation typology literature by challenging a widely held assumption that entrepreneurs have the potential to benefit from incubation and by reconceptualizing incubators as “crucibles” that perform a critical function in distinguishing high-potential entrepreneurs.
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