scholar.google.com › citations
This paper introduces a self-supervised augmentation tool for data agnostic representation learning, by quantizing each input channel through a non-uniform ...
Dec 19, 2022 · In this paper, we explore the orthogonal channel dimension for generic data augmentation by exploiting precision redundancy.
Our randomized quantization differs from these works, and augments data along the channel dimension. Quantization represents numerical values with a fixed dis-.
In this paper, we explore the orthogonal channel dimension for generic data augmentation. The data for each channel is quantized through a non-uniform quantizer ...
Our randomized quantization differs from these works, and augments data along the channel dimension. Quantization represents numerical values with a fixed dis-.
Dec 19, 2022 · 在本文中，我们探索了通用数据增强的正交通道维度。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。从另一个角度来看， ...
Randomized Quantization: A Generic Augmentation for Data Agnostic Self-supervised Learning ... learning has revolutionized unsupervised representation learning ...
Explore all code implementations available for Randomized Quantization for Data Agnostic Representation Learning.
Randomized Quantization: A Generic Augmentation for Data Agnostic Self-supervised Learning. H Wu, C Lei, X Sun, PS Wang, Q Chen, KT Cheng, S Lin, Z Wu.
This paper introduces a self-supervised representation learning approach by learning high-quality data representations through reconstructing random data ...