Abstract
Post-Training Quantization (PTQ) has received significant attention because
it requires only a small set of calibration data to quantize a full-precision
model, which is more practical in real-world applications in which full access
to a large training set is not available. However, it often leads to
overfitting on the small calibration dataset. Several methods have been
proposed to address this issue, yet they still rely on only the calibration set
for the quantization and they do not validate the quantized model due to the
lack of a validation set. In this work, we propose a novel meta-learning based
approach to enhance the performance of post-training quantization.
Specifically, to mitigate the overfitting problem, instead of only training the
quantized model using the original calibration set without any validation
during the learning process as in previous PTQ works, in our approach, we both
train and validate the quantized model using two different sets of images. In
particular, we propose a meta-learning based approach to jointly optimize a
transformation network and a quantized model through bi-level optimization. The
transformation network modifies the original calibration data and the modified
data will be used as the training set to learn the quantized model with the
objective that the quantized model achieves a good performance on the original
calibration data. Extensive experiments on the widely used ImageNet dataset
with different neural network architectures demonstrate that our approach
outperforms the state-of-the-art PTQ methods.