The Wide-field Infrared Survey Explorer (WISE) has detected hundreds of millions of sources over the entire sky. However, classifying them reliably is a great challenge due to degeneracies in WISE multicolor space and low detection levels in its two longest-wavelength bandpasses. In this paper, the deep learning classification network, IICnet (Infrared Image Classification network), is designed to classify sources from WISE images to achieve a more accurate classification goal. IICnet shows good ability on the feature extraction of the WISE sources. Experiments demonstrates that the classification results of IICnet are superior to some other methods; it has obtained 96.2% accuracy for galaxies, 97.9% accuracy for quasars, and 96.4% accuracy for stars, and the Area Under Curve (AUC) of the IICnet classifier can reach more than 99%. In addition, the superiority of IICnet in processing infrared images has been demonstrated in the comparisons with VGG16, GoogleNet, ResNet34, MobileNet, EfficientNetV2, and RepVGG-fewer parameters and faster inference. The above proves that IICnet is an effective method to classify infrared sources.

翻译：广域红外巡天探测器（WISE）在全天探测到了数亿个源。然而，由于WISE多色空间中的简并性以及其两个最长波长波段中的低探测水平，可靠地对它们进行分类是一个巨大挑战。本文设计了深度学习分类网络IICnet（红外图像分类网络），用于对WISE图像中的源进行分类，以实现更准确的分类目标。IICnet在WISE源的特征提取方面表现出良好的能力。实验证明，IICnet的分类结果优于其他一些方法；它对星系的准确率达到96.2%，对类星体的准确率达到97.9%，对恒星的准确率达到96.4%，且IICnet分类器的曲线下面积（AUC）可达99%以上。此外，在与VGG16、GoogleNet、ResNet34、MobileNet、EfficientNetV2和RepVGG的比较中，IICnet在处理红外图像方面表现出参数更少、推理更快的优势。以上证明IICnet是一种有效的红外源分类方法。