Wu Chong, Feng Zhenan, Zheng Jiangbin, Zhang Houwang
Department of Electrical Engineering, City University of Hong Kong, Kowloon, Hong Kong.
School of Automation, China University of Geosciences, Wuhan 430074, China.
Comput Intell Neurosci. 2020 Jul 10;2020:8842221. doi: 10.1155/2020/8842221. eCollection 2020.
In this paper, we want to find out whether gender bias will affect the success and whether there are some common laws driving the success in show business. We design an experiment, set the gender and productivity of an actor or actress in a certain period as the independent variables, and introduce deep learning techniques to do the prediction of success, extract the latent features, and understand the data we use. Three models have been trained: the first one is trained by the data of an actor, the second one is trained by the data of an actress, and the third one is trained by the mixed data. Three benchmark models are constructed with the same conditions. The experiment results show that our models are more general and accurate than benchmarks. An interesting finding is that the models trained by the data of an actor/actress only achieve similar performance on the data of another gender without performance loss. It shows that the gender bias is weakly related to success. Through the visualization of the feature maps in the embedding space, we see that prediction models have learned some common laws although they are trained by different data. Using the above findings, a more general and accurate model to predict the success in show business can be built.
在本文中，我们想要探究性别偏见是否会影响演艺事业的成功，以及是否存在一些驱动演艺事业成功的普遍规律。我们设计了一个实验，将某一时期演员的性别和产出作为自变量，并引入深度学习技术来进行成功预测、提取潜在特征以及理解我们所使用的数据。我们训练了三个模型：第一个由男演员的数据进行训练，第二个由女演员的数据进行训练，第三个由混合数据进行训练。在相同条件下构建了三个基准模型。实验结果表明，我们的模型比基准模型更具通用性和准确性。一个有趣的发现是，仅由男演员/女演员数据训练的模型在另一性别的数据上仅实现了相似的性能，且没有性能损失。这表明性别偏见与成功的关联较弱。通过对嵌入空间中特征图的可视化，我们发现预测模型虽然由不同数据训练，但已经学到了一些共同规律。利用上述发现，可以构建一个更具通用性和准确性的模型来预测演艺事业的成功。
