# Personalization of Deep Learning

arXiv: https://arxiv.org/abs/1909.02803 · HTML: https://mlsanity.com/paper/1909.02803/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2019.

## Built on by (most cited first, 18 of 18)

- [Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions](https://mlsanity.com/paper/2310.19775/) (2023)
- [Parameterized Knowledge Transfer for Personalized Federated Learning](https://mlsanity.com/paper/2111.02862/) (2021)
- [CD2-pFed: Cyclic Distillation-guided Channel Decoupling for Model Personalization in Federated Learning](https://mlsanity.com/paper/2204.03880/) (2022)
- [Exploring Safety-Utility Trade-Offs in Personalized Language Models](https://mlsanity.com/paper/2406.11107/) (2024)
- [The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation](https://mlsanity.com/paper/2301.08968/) (2023)
- [Test-Time Personalization with Meta Prompt for Gaze Estimation](https://mlsanity.com/paper/2401.01577/) (2024)
- [Towards Trustworthy AutoGrading of Short, Multi-lingual, Multi-type Answers](https://mlsanity.com/paper/2201.03425/) (2022)
- [Exploiting Personalized Invariance for Better Out-of-distribution Generalization in Federated Learning](https://mlsanity.com/paper/2211.11243/) (2022)
- [Beyond One-Size-Fits-All: A Survey of Personalized Affective Computing in Human-Agent Interaction](https://mlsanity.com/paper/2304.00377/) (2023)
- [FedSheafHN: Personalized Federated Learning on Graph-structured Data](https://mlsanity.com/paper/2405.16056/) (2024)
- [Improving Personalisation in Valence and Arousal Prediction using Data Augmentation](https://mlsanity.com/paper/2404.09042/) (2024)
- [GazeGen: Gaze-Driven User Interaction for Visual Content Generation](https://mlsanity.com/paper/2411.04335/) (2024)
- [Personalized Subgraph Federated Learning with Sheaf Collaboration](https://mlsanity.com/paper/2508.13642/) (2025)
- [Personalized Prediction By Learning Halfspace Reference Classes Under Well-Behaved Distribution](https://mlsanity.com/paper/2509.15592/) (2025)
- [Targeted Deep Learning: Framework, Methods, and Applications](https://mlsanity.com/paper/2105.14052/) (2021)
- [A Federated Learning-enabled Smart Street Light Monitoring Application: Benefits and Future Challenges](https://mlsanity.com/paper/2208.12996/) (2022)
- [Adaptive Dual-Mode Distillation with Incentive Schemes for Scalable, Heterogeneous Federated Learning on Non-IID Data](https://mlsanity.com/paper/2509.22507/) (2025)
- [Representation-Aligned Multi-Scale Personalization for Federated Learning](https://mlsanity.com/paper/2604.11278/) (2026)

## Abstract (arXiv)

We discuss training techniques, objectives and metrics toward personalization of deep learning models. In machine learning, personalization addresses the goal of a trained model to target a particular individual by optimizing one or more performance metrics, while conforming to certain constraints. To personalize, we investigate three methods of ``curriculum learning`` and two approaches for data grouping, i.e., augmenting the data of an individual by adding similar data identified with an auto-encoder. We show that both ``curriculuum learning'' and ``personalized'' data augmentation lead to improved performance on data of an individual. Mostly, this comes at the cost of reduced performance on a more general, broader dataset.