Self-supervised representation learning follows a paradigm of withholding some part of the data and tasking the network to predict it from the remaining part. Among many techniques, data augmentation lies at the core for creating the information gap. Towards this end, masking has emerged as a generic and powerful tool where content is withheld along the sequential dimension, e.g., spatial in images, temporal in audio, and syntactic in language. In this paper, we explore the orthogonal channel dimension for generic data augmentation by exploiting precision redundancy. The data for each channel is quantized through a non-uniform quantizer, with the quantized value sampled randomly within randomly sampled quantization bins. From another perspective, quantization is analogous to channel-wise masking, as it removes the information within each bin, but preserves the information across bins. Our approach significantly surpasses existing generic data augmentation methods, while showing on par performance against modality-specific augmentations. We comprehensively evaluate our approach on vision, audio, 3D point clouds, as well as the DABS benchmark which is comprised of various data modalities. The code is available at https: //github.com/microsoft/random_quantize.

翻译：自监督表示学习遵循一种范式：保留部分数据，并让网络从剩余部分预测这些数据。在众多技术中，数据增强是创建信息差异的核心手段。为此，掩码已成为一种通用且强大的工具，通过在序列维度（如图像中的空间维度、音频中的时间维度、语言中的句法维度）隐藏内容来实现信息差异。本文探索了通过利用精度冗余在正交通道维度上进行通用数据增强的方法。每个通道的数据通过非均匀量化器进行量化，量化值在随机采样的量化区间内随机采样产生。从另一个角度看，量化类似于通道维度的掩码，因为它移除了每个区间内的信息，但保留了区间间的信息。我们的方法显著超越了现有的通用数据增强方法，同时与特定模态的增强方法性能相当。我们在视觉、音频、3D点云以及包含多种数据模态的DABS基准测试上全面评估了该方法。代码已开源在https://github.com/microsoft/random_quantize。