基于可微近似白化的慢特征分析训练
Gradient-based Training of Slow Feature Analysis by Differentiable Approximate Whitening
摘要：
我们提出了功率慢特征分析，一种基于梯度的方法，从高维输入流中提取时间上慢的特征，该输入流在更快的时间尺度上变化，作为慢特征分析（SFA）的变体，允许任意可微架构的端到端训练，从而显着扩展了可有效用于慢特征提取的模型类别。我们提供的实验证据表明，PowerSFA是能够提取有意义的和信息丰富的低维特征的情况下（a）合成低维数据，（b）自我视觉数据，以及（c）一个一般的数据集，可以定义点之间的对称非时间相似性。
英文摘要：
We propose Power Slow Feature Analysis, a gradient-based method to extract temporally slow features from a high-dimensional input stream that varies on a faster time-scale, as a variant of Slow Feature Analysis (SFA) that allows end-to-end training of arbitrary differentiable architectures and thereby significantly extends the class of models that can effectively be used for slow feature extraction. We provide experimental evidence that PowerSFA is able to extract meaningful and informative low-dimensional features in the case of (a) synthetic low-dimensional data, (b) ego-visual data, and also for (c) a general dataset for which symmetric non-temporal similarities between points can be defined.
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。
