# Gradual Tuning: a better way of Fine Tuning the parameters of a Deep Neural Network

arXiv: https://arxiv.org/abs/1711.10177 · HTML: https://mlsanity.com/paper/1711.10177/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2017.

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

- [Layout-Aware Representation Learning for Open-Set ID Fraud Discovery](https://mlsanity.com/paper/2605.05215/) (2026)

## Abstract (arXiv)

In this paper we present an alternative strategy for fine-tuning the parameters of a network. We named the technique Gradual Tuning. Once trained on a first task, the network is fine-tuned on a second task by modifying a progressively larger set of the network's parameters. We test Gradual Tuning on different transfer learning tasks, using networks of different sizes trained with different regularization techniques. The result shows that compared to the usual fine tuning, our approach significantly reduces catastrophic forgetting of the initial task, while still retaining comparable if not better performance on the new task.