Post-training quantization (PTQ) is a neural network compression technique that converts a
full-precision model into a quantized model using lower-precision data types. Although it can …

Data-free quantization (DFQ) recovers the performance of quantized network (Q) without the
original data, but generates the fake sample via a generator (G) by learning from full …

Zero-shot quantization (ZSQ) is promising for compressing and accelerating deep neural
networks when the data for training full-precision models are inaccessible. In ZSQ, network …

Deep neural networks (DNNs) have achieved great success in many object detection tasks.
However, such DNNS-based large object detection models are generally computationally …

Data-free quantization (DFQ) recovers the performance of quantized network (Q) without
accessing the real data, but generates the fake sample via a generator (G) by learning from …

Zero-shot quantization (ZSQ) using synthetic data is a key approach for post-training
quantization (PTQ) under privacy and security constraints. However, existing data …

Quantization is an effective way to compress neural networks. By reducing the bit width of
the parameters, the processing efficiency of neural network models at edge devices can be …

Quantization for model compression can efficiently reduce the network complexity and
storage requirement but the original training data is necessary to remedy the performance …