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.

本文提出了一种名为 IICnet 的深度学习分类网络用于分类 WISE 图像中的源，实验结果表明 IICnet 对 WISE 源的特征提取具有良好的能力，分类效果也优于其他方法，同时在处理红外图像方面也显示出优越性，是一种有效的红外源分类方法。

基于 WISE 红外数据的深度学习应用：恒星、星系和类星体分类

Deep Learning Applications Based on WISE Infrared Data: Classification  of Stars, Galaxies and Quasars

Reversible image conversion (RIC) aims to build a reversible transformation
between specific visual content (e.g., short videos) and an embedding image,
where the original content can be restored from the embedding when necessary.
This work develops Invertible Image Conversion Net (IICNet) as a generic
solution to various RIC tasks due to its strong capacity and task-independent
design. Unlike previous encoder-decoder based methods, IICNet maintains a
highly invertible structure based on invertible neural networks (INNs) to
better preserve the information during conversion. We use a relation module and
a channel squeeze layer to improve the INN nonlinearity to extract cross-image
relations and the network flexibility, respectively. Experimental results
demonstrate that IICNet outperforms the specifically-designed methods on
existing RIC tasks and can generalize well to various newly-explored tasks.
With our generic IICNet, we no longer need to hand-engineer task-specific
embedding networks for rapidly occurring visual content. Our source codes are
available at: this https URL

本论文提出了可逆图像转换网络 (IICNet)，采用可逆神经网络 (INNs) 结构，内含关系模块和通道压缩层，可成为不同 Reversible image conversion 任务的通用解决方案，可提取跨图像关系的非线性性和网络的灵活性，相较于之前的方法表现更优，同时在各种新探索任务中具有良好的泛化性能，是一种有效的可逆图像转换算法。