  Existing neural networks are memory-consuming and computationally intensive,
making deploying them challenging in resource-constrained environments.
However, there are various methods to improve their efficiency. Two such
methods are quantization, a well-known approach for network compression, and
re-parametrization, an emerging technique designed to improve model
performance. Although both techniques have been studied individually, there has
been limited research on their simultaneous application. To address this gap,
we propose a novel approach called RepQ, which applies quantization to
re-parametrized networks. Our method is based on the insight that the test
stage weights of an arbitrary re-parametrized layer can be presented as a
differentiable function of trainable parameters. We enable quantization-aware
training by applying quantization on top of this function. RepQ generalizes
well to various re-parametrized models and outperforms the baseline method LSQ
quantization scheme in all experiments.
