基于深度神经网络有效层微调的天体分类
Classification of Astronomical Bodies by Efficient Layer Fine-Tuning of Deep Neural Networks
摘要：
SDSS-IV数据集包含有关各种天文体的信息，如天文台捕获的Galerkin，恒星和类星体。受我们在深度多模态学习方面的工作的启发，我们利用迁移学习对SDSS-IV数据集进行分类，我们进一步扩展了对这些架构进行微调的研究，以研究分类场景中的效果。Resnet-50、DenseNet-121、VGG-16、Xception、EfficientNetB 2、MobileNetV 2和NasnetMobile等架构都是使用不同级别的逐层微调构建的。我们的研究结果表明，冻结所有具有Imagenet权重的层并添加最终可训练层可能不是最佳解决方案。此外，基线模型和具有更高数量的可训练层的模型在某些架构中表现类似。模型需要在不同的级别上进行微调，并且需要特定的训练比率才能使模型被称为理想模型。不同的架构对可训练层数量的变化有不同的反应。虽然DenseNet-121、Xception、EfficientNetB 2等模型达到了与接近完美的训练曲线相对一致的峰值准确度，但Resnet-50、VGG-16、MobileNetV 2和NasnetMobile等模型的峰值准确度较低，延迟，训练曲线拟合较差。还发现，尽管移动神经网络具有较小的参数和模型大小，但它们可能并不总是理想地部署在低计算设备上，因为它们具有一贯较低的验证精度。自定义的评估指标，如调整参数比率和调整层比率，用于模型评估。
英文摘要：
The SDSS-IV dataset contains information about various astronomical bodies such as Galaxies, Stars, and Quasars captured by observatories. Inspired by our work on deep multimodal learning, which utilized transfer learning to classify the SDSS-IV dataset, we further extended our research in the fine tuning of these architectures to study the effect in the classification scenario. Architectures such as Resnet-50, DenseNet-121 VGG-16, Xception, EfficientNetB2, MobileNetV2 and NasnetMobile have been built using layer wise fine tuning at different levels. Our findings suggest that freezing all layers with Imagenet weights and adding a final trainable layer may not be the optimal solution. Further, baseline models and models that have higher number of trainable layers performed similarly in certain architectures. Model need to be fine tuned at different levels and a specific training ratio is required for a model to be termed ideal. Different architectures had different responses to the change in the number of trainable layers w.r.t accuracies. While models such as DenseNet-121, Xception, EfficientNetB2 achieved peak accuracies that were relatively consistent with near perfect training curves, models such as Resnet-50,VGG-16, MobileNetV2 and NasnetMobile had lower, delayed peak accuracies with poorly fitting training curves. It was also found that though mobile neural networks have lesser parameters and model size, they may not always be ideal for deployment on a low computational device as they had consistently lower validation accuracies. Customized evaluation metrics such as Tuning Parameter Ratio and Tuning Layer Ratio are used for model evaluation.
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