社交媒体图像的设备过滤以实现高效存储
On-device Filtering of Social Media Images for Efficient Storage
摘要：
精心制作的图像，如模因，季节性问候等，今天充斥着社交媒体平台。这些最终开始占用智能手机的大量内部内存，并且用户要浏览数百张图像并删除这些合成图像变得很麻烦。为了解决这个问题，我们提出了一种基于卷积神经网络（CNN）的新方法，通过对这些合成图像进行分类并允许用户一次性删除它们来对社交媒体图像进行设备过滤。自定义模型使用依赖可分离卷积层来实现智能手机上的低推理时间。我们已经在各种相机图像数据集上对我们的模型进行了广泛的评估，以涵盖相机捕获的图像的大多数方面。各种合成的社交媒体图像也进行了测试。所提出的解决方案在Places-365数据集上实现了98.25%的准确率，在我们准备的包含30 K实例的合成图像数据集上实现了95.81%的准确率。
英文摘要：
Artificially crafted images such as memes, seasonal greetings, etc are flooding the social media platforms today. These eventually start occupying a lot of internal memory of smartphones and it gets cumbersome for the user to go through hundreds of images and delete these synthetic images. To address this, we propose a novel method based on Convolutional Neural Networks (CNNs) for the on-device filtering of social media images by classifying these synthetic images and allowing the user to delete them in one go. The custom model uses depthwise separable convolution layers to achieve low inference time on smartphones. We have done an extensive evaluation of our model on various camera image datasets to cover most aspects of images captured by a camera. Various sorts of synthetic social media images have also been tested. The proposed solution achieves an accuracy of 98.25% on the Places-365 dataset and 95.81% on the Synthetic image dataset that we have prepared containing 30K instances.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
