  Model quantization is known as a promising method to compress deep neural
networks, especially for inferences on lightweight mobile or edge devices.
However, model quantization usually requires access to the original training
data to maintain the accuracy of the full-precision models, which is often
infeasible in real-world scenarios for security and privacy issues. A popular
approach to perform quantization without access to the original data is to use
synthetically generated samples, based on batch-normalization statistics or
adversarial learning. However, the drawback of such approaches is that they
primarily rely on random noise input to the generator to attain diversity of
the synthetic samples. We find that this is often insufficient to capture the
distribution of the original data, especially around the decision boundaries.
To this end, we propose Qimera, a method that uses superposed latent embeddings
to generate synthetic boundary supporting samples. For the superposed
embeddings to better reflect the original distribution, we also propose using
an additional disentanglement mapping layer and extracting information from the
full-precision model. The experimental results show that Qimera achieves
state-of-the-art performances for various settings on data-free quantization.
Code is available at https://github.com/iamkanghyunchoi/qimera.
