论文标题
通过有效的深层神经网络进行微调来对天文机构进行分类
Classification of Astronomical Bodies by Efficient Layer Fine-Tuning of Deep Neural Networks
论文作者
论文摘要
SDSS-IV数据集包含有关各种天文机构的信息，例如星系捕获的星系，恒星和类星体。受到我们在深度多模式学习方面的工作的启发，该学习利用转移学习来对SDSS-IV数据集进行分类，我们进一步扩展了对这些体系结构进行微调的研究，以研究分类场景中的效果。诸如Resnet-50，Densenet-121 VGG-16，Xception，EdgitionNetB2，Mobilenetv2和Nasnetmobile之类的体系结构已使用不同级别的层微调构建。我们的发现表明，将所有图层用成像网重冻结并添加最终训练层可能不是最佳解决方案。此外，在某些架构中，具有较高可训练图层数量的基线模型和模型在某些架构中类似。需要以不同的级别进行微调，并且需要特定的训练比率才能将模型称为理想。不同的体系结构对可训练层W.R.T精度的变化的响应不同。尽管诸如Densenet-121，Xception，ExtricNetB2之类的模型达到了峰精度，这些峰精度与接近完美的训练曲线相对一致，但诸如Resnet-50，VGG-16，MobilenetV2和NasnetMobile之类的模型具有较低的，延迟的峰精度，而较低的峰精度与拟合训练曲线较差。还发现，尽管移动神经网络的参数和模型大小较小，但由于它们始终降低验证精度，因此它们可能并不总是适合在低计算设备上部署的理想选择。定制的评估指标（例如调谐参数比率和调谐层比率）用于模型评估。
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.
