论文标题
q-vae用于解开表示和潜在动力学系统的Q-VAE
q-VAE for Disentangled Representation Learning and Latent Dynamical Systems
论文作者
论文摘要
提出了从称为Q-VAE的TSALLIS统计数据得出的变异自动编码器（VAE）。在提出的方法中，采用标准VAE来统计地提取隐藏在采样数据中的潜在空间，并且该潜在空间有助于使机器人在可行的计算时间和成本中可控制。为了提高潜在空间的实用性，本文着重于分离的表示形式学习，例如$β$ -VAE，这是它的基线。从Tsallis统计学的角度开始，提出的新的Q-VAE的新下限是为了最大程度地提高采样数据的可能性，该数据可能被视为具有变形的Kullback-Leibler差异的自适应$β$ -VAE。为了验证提出的Q-VAE的好处，执行了从MNIST数据集中提取潜在空间的基准任务。结果表明，所提出的Q-VAE改善了分离的表示，同时保持了数据的重建精度。此外，它放宽了数据之间的独立条件，这可以通过学习非线性动力学系统的潜在动力学来证明。通过结合分解的表示，提出的Q-VAE从初始状态和动作序列实现了稳定且准确的长期状态预测。 Hexapod Walking的数据集可在IEEE DataPort，doi：https：//dx.doi.org/10.21227/99af-jw71上找到。
A variational autoencoder (VAE) derived from Tsallis statistics called q-VAE is proposed. In the proposed method, a standard VAE is employed to statistically extract latent space hidden in sampled data, and this latent space helps make robots controllable in feasible computational time and cost. To improve the usefulness of the latent space, this paper focuses on disentangled representation learning, e.g., $β$-VAE, which is the baseline for it. Starting from a Tsallis statistics perspective, a new lower bound for the proposed q-VAE is derived to maximize the likelihood of the sampled data, which can be considered an adaptive $β$-VAE with deformed Kullback-Leibler divergence. To verify the benefits of the proposed q-VAE, a benchmark task to extract the latent space from the MNIST dataset was performed. The results demonstrate that the proposed q-VAE improved disentangled representation while maintaining the reconstruction accuracy of the data. In addition, it relaxes the independency condition between data, which is demonstrated by learning the latent dynamics of nonlinear dynamical systems. By combining disentangled representation, the proposed q-VAE achieves stable and accurate long-term state prediction from the initial state and the action sequence. The dataset for hexapod walking is available on IEEE Dataport, doi: https://dx.doi.org/10.21227/99af-jw71.
