  In the realm of deep neural network deployment, low-bit quantization presents
a promising avenue for enhancing computational efficiency. However, it often
hinges on the availability of training data to mitigate quantization errors, a
significant challenge when data availability is scarce or restricted due to
privacy or copyright concerns. Addressing this, we introduce GenQ, a novel
approach employing an advanced Generative AI model to generate photorealistic,
high-resolution synthetic data, overcoming the limitations of traditional
methods that struggle to accurately mimic complex objects in extensive datasets
like ImageNet. Our methodology is underscored by two robust filtering
mechanisms designed to ensure the synthetic data closely aligns with the
intrinsic characteristics of the actual training data. In case of limited data
availability, the actual data is used to guide the synthetic data generation
process, enhancing fidelity through the inversion of learnable token
embeddings. Through rigorous experimentation, GenQ establishes new benchmarks
in data-free and data-scarce quantization, significantly outperforming existing
methods in accuracy and efficiency, thereby setting a new standard for
quantization in low data regimes.
