# L2PF -- Learning to Prune Faster

arXiv: https://arxiv.org/abs/2101.02663 · HTML: https://mlsanity.com/paper/2101.02663/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 23 references that are in the map.
- First posted on arXiv 7 Jan 2021.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015)
- [Channel Pruning for Accelerating Very Deep Neural Networks](https://mlsanity.com/paper/1707.06168/) (2017)
- [AMC: AutoML for Model Compression and Acceleration on Mobile Devices](https://mlsanity.com/paper/1802.03494/) (2018)
- [Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration](https://mlsanity.com/paper/1811.00250/) (2018)
- [Monte Carlo Gradient Estimation in Machine Learning](https://mlsanity.com/paper/1906.10652/) (2019)
- [NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications](https://mlsanity.com/paper/1804.03230/) (2018)
- [Adversarial Robustness vs Model Compression, or Both?](https://mlsanity.com/paper/1903.12561/) (2019)
- [Learning values across many orders of magnitude](https://mlsanity.com/paper/1602.07714/) (2016)
- [Learning to Prune Filters in Convolutional Neural Networks](https://mlsanity.com/paper/1801.07365/) (2018)
- [Binary DAD-Net: Binarized Driveable Area Detection Network for Autonomous Driving](https://mlsanity.com/paper/2006.08178/) (2020)
- [ALF: Autoencoder-based Low-rank Filter-sharing for Efficient Convolutional Neural Networks](https://mlsanity.com/paper/2007.13384/) (2020)

## Built on by (most cited first, 2 of 2)

- [Deep Neural Networks pruning via the Structured Perspective Regularization](https://mlsanity.com/paper/2206.14056/) (2022)
- [OWLed: Outlier-weighed Layerwise Pruning for Efficient Autonomous Driving Framework](https://mlsanity.com/paper/2411.07711/) (2024)

## Abstract (arXiv)

Various applications in the field of autonomous driving are based on convolutional neural networks (CNNs), especially for processing camera data. The optimization of such CNNs is a major challenge in continuous development. Newly learned features must be brought into vehicles as quickly as possible, and as such, it is not feasible to spend redundant GPU hours during compression. In this context, we present Learning to Prune Faster which details a multi-task, try-and-learn method, discretely learning redundant filters of the CNN and a continuous action of how long the layers have to be fine-tuned. This allows us to significantly speed up the convergence process of learning how to find an embedded-friendly filter-wise pruned CNN. For ResNet20, we have achieved a compression ratio of 3.84 x with minimal accuracy degradation. Compared to the state-of-the-art pruning method, we reduced the GPU hours by 1.71 x.