Recently, dataset distillation has paved the way towards efficient machine
learning, especially for image datasets. However, the distillation for videos,
characterized by an exclusive temporal dimension, remains an underexplored
domain. In this work, we provide the first systematic study of video
distillation and introduce a taxonomy to categorize temporal compression. Our
investigation reveals that the temporal information is usually not well learned
during distillation , and the temporal dimension of synthetic data contributes
little. The observations motivate our unified framework of disentangling the
dynamic and static information in the videos. It first distills the videos into
still images as static memory and then compensates the dynamic and motion
information with a learnable dynamic memory block. Our method achieves
state-of-the-art on video datasets at different scales, with notably smaller
storage expenditure. Our code will be publicly available.

通过对视频进行数据蒸馏，提出了一种方法来解决视频中的时间维度问题，该方法首先将视频转化为静态图像作为静态内存，然后使用可学习的动态内存块补偿动态和运动信息，以在不同规模的视频数据集上实现最先进的表现。

与图像共舞：通过静态动态解缠实现视频浓缩

Dancing with Images: Video Distillation via Static-Dynamic  Disentanglement

Deep learning (DL)-based channel state information (CSI) feedback methods
compressed the CSI matrix by exploiting its delay and angle features
straightforwardly, while the measure in terms of information contained in the
CSI matrix has rarely been considered. Based on this observation, we introduce
self-information as an informative CSI representation from the perspective of
information theory, which reflects the amount of information of the original
CSI matrix in an explicit way. Then, a novel DL-based network is proposed for
temporal CSI compression in the self-information domain, namely SD-CsiNet. The
proposed SD-CsiNet projects the raw CSI onto a self-information matrix in the
newly-defined self-information domain, extracts both temporal and spatial
features of the self-information matrix, and then couples these two features
for effective compression. Experimental results verify the effectiveness of the
proposed SD-CsiNet by exploiting the self-information of CSI. Particularly for
compression ratios 1/8 and 1/16, the SD-CsiNet respectively achieves 7.17 dB
and 3.68 dB performance gains compared to state-of-the-art methods.

提出了一种基于信息论的 CSI 压缩方法 SD-CsiNet，在 self-information 领域内建立了一个新的 DL 网络，可以在时间和空间方面提取 CSI self-information 矩阵的特征并将这两个特征整合起来进行压缩。在实验中展示了 SD-CsiNet 的有效性和性能比其他方法有显著提升（分别达到了 1/8 和 1/16 的压缩比下 7.17 dB 和 3.68 dB 的性能增益）。