Visualization refers to the representation of data or information in a graphical or pictorial format. It involves creating visual depictions of data to facilitate understanding, analysis, and communication. The goal of visualization is to present complex information in a more accessible and comprehensible manner, allowing individuals to identify patterns, trends, and insights that may be challenging to discern from raw data.

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S3VAE: Self-Supervised Sequential VAE for Representation Disentanglement and Data Generation

S3VAE: Self-Supervised Sequential VAE for Representation Disentanglement and Data Generation We propose a sequential variational autoencoder to learn disentangled representations of sequential data (e.g., videos and audios) under self-supervision. Specifically, we exploit the benefits of some readily accessible supervision signals from input data itself or some off-the-shelf functional models and accordingly design auxiliary tasks for our model to utilize these signals. With the supervision of the signals, our model can easily disentangle the representation of an input sequence into static factors and dynamic factors (i.e., time-invariant and time-varying parts). Comprehensive experiments across videos and audios verify the effectiveness of our model on representation disentanglement and generation of sequential data, and demonstrate that, our model with self-supervision performs comparable to, if not better than, the fully-supervised model with ground truth labels, and outperforms state-of-the-art unsupervised models by a large margin.