# Play and Prune: Adaptive Filter Pruning for Deep Model Compression

arXiv: https://arxiv.org/abs/1905.04446 · HTML: https://mlsanity.com/paper/1905.04446/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2019.

## Built on by (most cited first, 15 of 15)

- [Pruning and Quantization for Deep Neural Network Acceleration: A Survey](https://mlsanity.com/paper/2101.09671/) (2021)
- [Low-rank lottery tickets: finding efficient low-rank neural networks via matrix differential equations](https://mlsanity.com/paper/2205.13571/) (2022)
- [Sauron U-Net: Simple automated redundancy elimination in medical image segmentation via filter pruning](https://mlsanity.com/paper/2209.13590/) (2022)
- [On the Compression of Neural Networks Using ℓ0-Norm Regularization and Weight Pruning](https://mlsanity.com/paper/2109.05075/) (2021)
- [Neuron-based Pruning of Deep Neural Networks with Better Generalization using Kronecker Factored Curvature Approximation](https://mlsanity.com/paper/2111.08577/) (2021)
- [Harnessing Orthogonality to Train Low-Rank Neural Networks](https://mlsanity.com/paper/2401.08505/) (2024)
- [Pushing the Efficiency Limit Using Structured Sparse Convolutions](https://mlsanity.com/paper/2210.12818/) (2022)
- [AdaSpring: Context-adaptive and Runtime-evolutionary Deep Model Compression for Mobile Applications](https://mlsanity.com/paper/2101.11800/) (2021)
- [Finding Deviated Behaviors of the Compressed DNN Models for Image Classifications](https://mlsanity.com/paper/2112.02819/) (2021)
- [Edge AI for Internet of Energy: Challenges and Perspectives](https://mlsanity.com/paper/2311.16851/) (2023)
- [Efficient compression of neural networks and datasets](https://mlsanity.com/paper/2505.17469/) (2025)
- [STD-NET: Search of Image Steganalytic Deep-learning Architecture via Hierarchical Tensor Decomposition](https://mlsanity.com/paper/2206.05651/) (2022)
- [D-Score: A Synapse-Inspired Approach for Filter Pruning](https://mlsanity.com/paper/2308.04470/) (2023)
- [REFT: Resource-Efficient Federated Training Framework for Heterogeneous and Resource-Constrained Environments](https://mlsanity.com/paper/2308.13662/) (2023)
- [AB-Training: A Communication-Efficient Approach for Distributed Low-Rank Learning](https://mlsanity.com/paper/2405.01067/) (2024)

## Abstract (arXiv)

While convolutional neural networks (CNN) have achieved impressive performance on various classification/recognition tasks, they typically consist of a massive number of parameters. This results in significant memory requirement as well as computational overheads. Consequently, there is a growing need for filter-level pruning approaches for compressing CNN based models that not only reduce the total number of parameters but reduce the overall computation as well. We present a new min-max framework for filter-level pruning of CNNs. Our framework, called Play and Prune (PP), jointly prunes and fine-tunes CNN model parameters, with an adaptive pruning rate, while maintaining the model's predictive performance. Our framework consists of two modules: (1) An adaptive filter pruning (AFP) module, which minimizes the number of filters in the model; and (2) A pruning rate controller (PRC) module, which maximizes the accuracy during pruning. Moreover, unlike most previous approaches, our approach allows directly specifying the desired error tolerance instead of pruning level. Our compressed models can be deployed at run-time, without requiring any special libraries or hardware. Our approach reduces the number of parameters of VGG-16 by an impressive factor of 17.5X, and number of FLOPS by 6.43X, with no loss of accuracy, significantly outperforming other state-of-the-art filter pruning methods.