Aiming at the problem that the traditional algorithm is easy to overfitting, which leads to low prediction accuracy of the model. This paper designs a traffic accident impact range prediction model based on CatBoost ensemble algorithm. The model uses linear fitting for range prediction and uses the ordered boosting method to introduce the prior term and weight coefficient. It can automatically adjust dynamically in each calculation, so as to effectively avoid the condition offset and gradient deviation and reduce the overfitting. Under small-scale training, the algorithm can achieve high accuracy prediction and has strong generalization ability.
KEYWORDS: Video, Transformers, Performance modeling, Data modeling, Visual process modeling, Feature extraction, Optical flow, Video coding, Image segmentation, Deep learning
Deepfake open source technology has lowered the threshold for AI face swapping to a very low level, making it possible to swap faces with one click. The cost of "disinformation" is greatly reduced, so that some deeply faked pictures and videos can be spread on social networks The social network can spread explosively. However, in the defense layer, there are almost no standardized and automated detection tools for deepfake. There is no such tool. Therefore, whether for individuals or platforms, the time window for fighting fake and disinformation is very short, but it is very difficult. In this paper, we use the Transformer model as a base, improve the model and optimize the structure of the model, so that the model can extract the depth features of the video and build a more accurate and efficient deepfake inspection method.
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