English(EN) Improved Knowledge Distillation for Land-Use Image Classification

新的知识蒸馏方法提高了土地利用图像分类的准确性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-16 04:00

研究人员开发了一种改进的知识蒸馏框架，用于压缩用于土地利用图像分类的深度卷积神经网络。该方法采用教师-学生学习范式，其中 VGG16 网络将知识转移到 MobileNetV2 模型。通过结合来自真实标签的硬监督和使用 Kullback-Leibler 散度和余弦相似度损失的软监督，该方法在土地利用数据集上实现了 99.04% 的准确率，优于基线方法，同时显著压缩了模型。 AI

排序理由该集群包含一篇学术论文，详细介绍了知识蒸馏在图像分类中的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Arundhuti Sur, Abhiroop Chatterjee, Susmita Ghosh, Emmett Ientilucci · 2026-06-16 04:00

改进的知识蒸馏用于土地利用图像分类

arXiv:2606.14886v1 Announce Type: cross Abstract: In the present article, an improved Knowledge Distillation (KD) framework has been proposed for efficient compression of deep convolutional neural networks for land-use image classification task. Motivated by the need to achieve c…

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改进的知识蒸馏用于土地利用图像分类

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