# Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks

arXiv: https://arxiv.org/abs/1906.06033 · HTML: https://mlsanity.com/paper/1906.06033/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2019.

## Built on by (most cited first, 5 of 5)

- [Training Transformers with Enforced Lipschitz Constants](https://mlsanity.com/paper/2507.13338/) (2025)
- [MixQuant: Mixed Precision Quantization with a Bit-width Optimization Search](https://mlsanity.com/paper/2309.17341/) (2023)
- [Deep Recursive Embedding for High-Dimensional Data](https://mlsanity.com/paper/2104.05171/) (2021)
- [Deep Recursive Embedding for High-Dimensional Data](https://mlsanity.com/paper/2111.00622/) (2021)
- [Progressive Multi-Ancestor Bit-Depth Distillation](https://mlsanity.com/paper/2610.04100/) (2026)

## Abstract (arXiv)

The deep layers of modern neural networks extract a rather rich set of features as an input propagates through the network. This paper sets out to harvest these rich intermediate representations for quantization with minimal accuracy loss while significantly reducing the memory footprint and compute intensity of the DNN. This paper utilizes knowledge distillation through teacher-student paradigm (Hinton et al., 2015) in a novel setting that exploits the feature extraction capability of DNNs for higher-accuracy quantization. As such, our algorithm logically divides a pretrained full-precision DNN to multiple sections, each of which exposes intermediate features to train a team of students independently in the quantized domain. This divide and conquer strategy, in fact, makes the training of each student section possible in isolation while all these independently trained sections are later stitched together to form the equivalent fully quantized network. Our algorithm is a sectional approach towards knowledge distillation and is not treating the intermediate representation as a hint for pretraining before one knowledge distillation pass over the entire network (Romero et al., 2015). Experiments on various DNNs (AlexNet, LeNet, MobileNet, ResNet-18, ResNet-20, SVHN and VGG-11) show that, this approach -- called DCQ (Divide and Conquer Quantization) -- on average, improves the performance of a state-of-the-art quantized training technique, DoReFa-Net (Zhou et al., 2016) by 21.6% and 9.3% for binary and ternary quantization, respectively. Additionally, we show that incorporating DCQ to existing quantized training methods leads to improved accuracies as compared to previously reported by multiple state-of-the-art quantized training methods.