Abstract Machine learning methods strive to acquire a robust model during the training
process that can effectively generalize to test samples, even in the presence of distribution …

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 …

Learning to synthesize data has emerged as a promising direction in zero-shot quantization
(ZSQ), which represents neural networks by low-bit integer without accessing any of the real …

Neural network quantization is a very promising solution in the field of model compression,
but its resulting accuracy highly depends on a training/fine-tuning process and requires the …

Abstract Model quantization is known as a promising method to compress deep neural
networks, especially for inferences on lightweight mobile or edge devices. However, model …

Abstract Data-free Knowledge Distillation (DFKD) has gained popularity recently, with the
fundamental idea of carrying out knowledge transfer from a Teacher neural network to a …

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 …

Quantization of deep neural networks (DNN) has been proven effective for compressing and
accelerating DNN models. Data-free quantization (DFQ) is a promising approach without the …

Generative data-free quantization emerges as a practical compression approach that
quantizes deep neural networks to low bit-width without accessing the real data. This …

Abstract Model quantization is considered as a promising method to greatly reduce the
resource requirements of deep neural networks. To deal with the performance drop induced …