用于视频帧预测的复值门控自动编码器
Complex Valued Gated Auto-encoder for Video Frame Prediction
摘要：
近年来，复值人工神经网络获得了越来越多的关注，因为它们允许神经网络学习更丰富的表示，同时潜在地结合更少的参数。特别是在计算机图形学领域，许多传统的运算都严重依赖于复数域的计算，因此复值神经网络有着天然的应用。在本文中，我们执行帧预测的视频序列使用一个复杂的价值门控自动编码器。首先，我们的方法的动机显示傅立叶变换可以被看作是平移操作的基础。然后，我们提出了一个复杂的神经网络可以学习这样的转换，并比较其性能和参数效率的实值门控自动编码器。此外，我们还展示了如何通过使用卷积单元来扩展实值和复值神经网络，从而显着提高预测性能和参数效率。该网络在移动噪声和弹跳球数据集上进行评估。
英文摘要：
In recent years, complex valued artificial neural networks have gained increasing interest as they allow neural networks to learn richer representations while potentially incorporating less parameters. Especially in the domain of computer graphics, many traditional operations rely heavily on computations in the complex domain, thus complex valued neural networks apply naturally. In this paper, we perform frame predictions in video sequences using a complex valued gated auto-encoder. First, our method is motivated showing how the Fourier transform can be seen as the basis for translational operations. Then, we present how a complex neural network can learn such transformations and compare its performance and parameter efficiency to a real-valued gated autoencoder. Furthermore, we show how extending both - the real and the complex valued - neural networks by using convolutional units can significantly improve prediction performance and parameter efficiency. The networks are assessed on a moving noise and a bouncing ball dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
