  One-hot vectors, a method for representing discrete/categorical data, are
commonly used in machine learning due to their simplicity and intuitiveness.
However, the one-hot vectors suffer from a linear increase in dimensionality,
posing computational and memory challenges, especially when dealing with
datasets containing numerous categories. To address this issue, we propose
Residual Bit Vectors (ResBit), a technique for densely representing categorical
data. While Analog Bits presents a similar approach, it faces challenges in
categorical data generation tasks. ResBit overcomes these limitations, offering
a more versatile solution. In our experiments, we focus on tabular data
generation, examining the performance across scenarios with varying amounts of
categorical data. We verify the acceleration and ensure the maintenance or
improvement of performance.
