Second, to resist overfitting issues caused by few training samples, a hyper-class embedding is learned by clustering all category embeddings for initialization and aligned with category embedding of the new class for enhancement, where learned knowledge assists to learn new knowledge, thus alleviating performance dependence on training data scale.
In this way, a criterion is introduced that is used together with accuracy and FPR criteria for malware analysis in IoT environment.
To the best of the authors knowledge, this is the first time that LGSO algorithms are applied to the optimal power allocation problem in IoT networks.
Progressive growing enhances image resolution gradually, thereby preserving precision of recovered image.
In recent years, deep learning methods have achieved impressive results with higher peak signal-to-noise ratio in single image super-resolution (SISR) tasks by utilizing deeper layers.
Recently significant performance improvement in face detection was made possible by deeply trained convolutional networks.