# FisherMask: Enhancing Neural Network Labeling Efficiency in Image Classification Using Fisher Information

arXiv: https://arxiv.org/abs/2411.05752 · HTML: https://mlsanity.com/paper/2411.05752/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 24 references that are in the map.
- First posted on arXiv 8 Nov 2024.

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

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds](https://mlsanity.com/paper/1906.03671/) (2019)
- [Training Neural Networks with Fixed Sparse Masks](https://mlsanity.com/paper/2111.09839/) (2021)
- [Deep Active Learning: Unified and Principled Method for Query and Training](https://mlsanity.com/paper/1911.09162/) (2019)
- [Diverse mini-batch Active Learning](https://mlsanity.com/paper/1901.05954/) (2019)
- [Gone Fishing: Neural Active Learning with Fisher Embeddings](https://mlsanity.com/paper/2106.09675/) (2021)
- [Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantities](https://mlsanity.com/paper/2208.00549/) (2022)
- [Convergence Rates of Active Learning for Maximum Likelihood Estimation](https://mlsanity.com/paper/1506.02348/) (2015)
- [Entropic Open-set Active Learning](https://mlsanity.com/paper/2312.14126/) (2023)
- [Active Learning for Deep Visual Tracking](https://mlsanity.com/paper/2110.13259/) (2021)
- [Asymptotic Analysis of Objectives based on Fisher Information in Active Learning](https://mlsanity.com/paper/1605.08798/) (2016)
- [Supervised Anomaly Detection via Conditional Generative Adversarial Network and Ensemble Active Learning](https://mlsanity.com/paper/2104.11952/) (2021)
- [Active Learning by Query by Committee with Robust Divergences](https://mlsanity.com/paper/2211.10013/) (2022)
- [Compute-Efficient Active Learning](https://mlsanity.com/paper/2401.07639/) (2024)
- [Ocean Data Quality Assessment through Outlier Detection-enhanced Active Learning](https://mlsanity.com/paper/2312.10817/) (2023)

## Built on by (most cited first, 3 of 3)

- [LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model Training](https://mlsanity.com/paper/2411.15217/) (2024)
- [Prototype Fusion: A Training-Free Multi-Layer Approach to OOD Detection](https://mlsanity.com/paper/2603.23677/) (2026)
- [Learning What Can Be Picked: Active Reachability Estimation for Efficient Robotic Fruit Harvesting](https://mlsanity.com/paper/2603.23679/) (2026)

## Abstract (arXiv)

Deep learning (DL) models are popular across various domains due to their remarkable performance and efficiency. However, their effectiveness relies heavily on large amounts of labeled data, which are often time-consuming and labor-intensive to generate manually. To overcome this challenge, it is essential to develop strategies that reduce reliance on extensive labeled data while preserving model performance. In this paper, we propose FisherMask, a Fisher information-based active learning (AL) approach that identifies key network parameters by masking them based on their Fisher information values. FisherMask enhances batch AL by using Fisher information to select the most critical parameters, allowing the identification of the most impactful samples during AL training. Moreover, Fisher information possesses favorable statistical properties, offering valuable insights into model behavior and providing a better understanding of the performance characteristics within the AL pipeline. Our extensive experiments demonstrate that FisherMask significantly outperforms state-of-the-art methods on diverse datasets, including CIFAR-10 and FashionMNIST, especially under imbalanced settings. These improvements lead to substantial gains in labeling efficiency. Hence serving as an effective tool to measure the sensitivity of model parameters to data samples. Our code is available on urlhttps://github.com/sgchr273/FisherMask.