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Gates is also expected to provide details on Microsoft's latest effort to popularize an automotive version of its Windows operating system. The software giant has developed a specialized version of Windows CE to power automotive devices such as navigation systems and music players.
HP CEO Carly Fiorina takes the stage Thursday for a speech expected to include details of the computing giant's new consumer push, which includes selling high-end TV sets and running a music download service. HP's calculator division is also promising big news.
Mark Oldani, director of U.S. consumer marketing for Dell, said the PC giant isn't jumping willy-nilly into gadgets. "Our plan is not to go into a broad range of offerings," he said. "We want to be fairly targeted in our approach. We want to focus on areas where we can think we can add the most value for the customer."
In response to the above image collection problems, we screened the dataset. At the same time, for the convenience of defining uniform selection conditions, we chose the face of the giant panda as the focus of the study. Since faces contain many unique attributes and have been previously applied in facial age recognition (Antipov et al., 2017; Levi & Hassncer, 2015; Samek et al., 2017) and individual panda recognition studies (Chen et al., 2020; Hou et al., 2019, 2020).
For giant panda images, a deep neural network needed to be constructed to extract features at a deep level; however, the training of deep neural networks faced network degradation problems. Residual learning effectively solved this problem and can train neural networks with more than 1000 layers (He et al., 2016a). In the case that the network is getting deeper and deeper, the residual connection and identity mapping in the residual block (He et al., 2016b) can effectively avoid gradient disappearance and gradient explosion, and it is easier to optimize and improve the accuracy. Therefore, in this paper, ResNet (He et al., 2016a) was introduced as one of the experimental models for the age group classification of giant pandas. In addition, in order to further improve the accuracy and speed of the network, it is necessary to balance the three dimensions of width, depth, and resolution, EfficientNet (Tan & Le, 2019) was also introduced in this paper as an experimental model for the age group classification of giant pandas.
As the focus of this study was giant panda facial images, in order to verify the feasibility of the deep learning technology, we first crop the face according to the data annotation (Figure 5). The purpose was to reduce the influence of the background and let the model pay more attention to the learning of the foreground.
The model we employed had a better classification performance for the age group of juvenile and adult giant pandas, followed by the subadult group, and the weakest was in classifying the elderly panda age group (Figure 9). This may be since the fact that of the aging of giant pandas, their facial features gradually tend to be stable, making it difficult to distinguish the facial features of subadult from adult giant pandas, as well as adult and elderly giant pandas. 2ff7e9595c
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