# Distilled Pruning: Using Synthetic Data to Win the Lottery

arXiv: https://arxiv.org/abs/2307.03364 · HTML: https://mlsanity.com/paper/2307.03364/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 19 references that are in the map.
- First posted on arXiv 7 Jul 2023.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks](https://mlsanity.com/paper/1803.03635/) (2018)
- [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015)
- [Linear Mode Connectivity and the Lottery Ticket Hypothesis](https://mlsanity.com/paper/1912.05671/) (2019)
- [Dataset Distillation by Matching Training Trajectories](https://mlsanity.com/paper/2203.11932/) (2022)
- [Dataset Meta-Learning from Kernel Ridge-Regression](https://mlsanity.com/paper/2011.00050/) (2020)
- [Dataset Distillation using Neural Feature Regression](https://mlsanity.com/paper/2206.00719/) (2022)
- [Data Distillation: A Survey](https://mlsanity.com/paper/2301.04272/) (2023)
- [Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot?](https://mlsanity.com/paper/2107.00166/) (2021)
- [Dataset Distillation with Convexified Implicit Gradients](https://mlsanity.com/paper/2302.06755/) (2023)
- [Unmasking the Lottery Ticket Hypothesis: What's Encoded in a Winning Ticket's Mask?](https://mlsanity.com/paper/2210.03044/) (2022)
- [What can linear interpolation of neural network loss landscapes tell us?](https://mlsanity.com/paper/2106.16004/) (2021)
- [The Elastic Lottery Ticket Hypothesis](https://mlsanity.com/paper/2103.16547/) (2021)
- [Lottery Tickets on a Data Diet: Finding Initializations with Sparse Trainable Networks](https://mlsanity.com/paper/2206.01278/) (2022)
- [Bespoke vs. Pret-`a-Porter Lottery Tickets: Exploiting Mask Similarity for Trainable Sub-Network Finding](https://mlsanity.com/paper/2007.04091/) (2020)

## Built on by (most cited first, 1 of 1)

- [Linear Mode Connectivity in Sparse Neural Networks](https://mlsanity.com/paper/2310.18769/) (2023)

## Abstract (arXiv)

This work introduces a novel approach to pruning deep learning models by using distilled data. Unlike conventional strategies which primarily focus on architectural or algorithmic optimization, our method reconsiders the role of data in these scenarios. Distilled datasets capture essential patterns from larger datasets, and we demonstrate how to leverage this capability to enable a computationally efficient pruning process. Our approach can find sparse, trainable subnetworks (a.k.a. Lottery Tickets) up to 5x faster than Iterative Magnitude Pruning at comparable sparsity on CIFAR-10. The experimental results highlight the potential of using distilled data for resource-efficient neural network pruning, model compression, and neural architecture search.