论文标题
数据和计算有限资源深度学习的高效设计
A Data and Compute Efficient Design for Limited-Resources Deep Learning
论文作者
论文摘要
由于它们提高了数据效率，地位神经网络引起了对深度学习社区的兴趣。它们已成功地应用于医疗领域，在该域中，可以有效利用数据中的对称性来构建更准确，更健壮的模型。为了能够到达更大的患者，已为医疗应用开发了移动，在进行的深度学习解决方案的实施。但是，通常使用大型和计算昂贵的体系结构实现了模型，不适合在移动设备上运行。在这项工作中，我们设计和测试了Mobilenetv2的模棱两可的版本，并通过模型量化进一步对其进行了优化，以实现更有效的推断。我们在贴图Camelyon（PCAM）医疗数据集上实现了最接近的最直接性能，同时更有效地进行了计算。
Thanks to their improved data efficiency, equivariant neural networks have gained increased interest in the deep learning community. They have been successfully applied in the medical domain where symmetries in the data can be effectively exploited to build more accurate and robust models. To be able to reach a much larger body of patients, mobile, on-device implementations of deep learning solutions have been developed for medical applications. However, equivariant models are commonly implemented using large and computationally expensive architectures, not suitable to run on mobile devices. In this work, we design and test an equivariant version of MobileNetV2 and further optimize it with model quantization to enable more efficient inference. We achieve close-to state of the art performance on the Patch Camelyon (PCam) medical dataset while being more computationally efficient.
