# Diversity in Machine Learning

arXiv: https://arxiv.org/abs/1807.01477 · HTML: https://mlsanity.com/paper/1807.01477/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2018.

## Built on by (most cited first, 30 of 48)

- [Open Domain Generalization with Domain-Augmented Meta-Learning](https://mlsanity.com/paper/2104.03620/) (2021)
- [LLMParser: An Exploratory Study on Using Large Language Models for Log Parsing](https://mlsanity.com/paper/2404.18001/) (2024)
- [Quality Matters: Evaluating Synthetic Data for Tool-Using LLMs](https://mlsanity.com/paper/2409.16341/) (2024)
- [Black-Box Testing of Deep Neural Networks Through Test Case Diversity](https://mlsanity.com/paper/2112.12591/) (2021)
- [Improving Group Robustness on Spurious Correlation Requires Preciser Group Inference](https://mlsanity.com/paper/2404.13815/) (2024)
- [DeepGD: A Multi-Objective Black-Box Test Selection Approach for Deep Neural Networks](https://mlsanity.com/paper/2303.04878/) (2023)
- [Experimental Standards for Deep Learning in Natural Language Processing Research](https://mlsanity.com/paper/2204.06251/) (2022)
- [Towards Semantic Communication Protocols: A Probabilistic Logic Perspective](https://mlsanity.com/paper/2207.03920/) (2022)
- [Accelerating Batch Active Learning Using Continual Learning Techniques](https://mlsanity.com/paper/2305.06408/) (2023)
- [Spatial LibriSpeech: An Augmented Dataset for Spatial Audio Learning](https://mlsanity.com/paper/2308.09514/) (2023)
- [VulScribeR: Exploring RAG-based Vulnerability Augmentation with LLMs](https://mlsanity.com/paper/2408.04125/) (2024)
- [Addressing the Intra-class Mode Collapse Problem using Adaptive Input Image Normalization in GAN-based X-ray Images](https://mlsanity.com/paper/2201.10324/) (2022)
- [XPROAX-Local explanations for text classification with progressive neighborhood approximation](https://mlsanity.com/paper/2109.15004/) (2021)
- [BELT:Bootstrapping Electroencephalography-to-Language Decoding and Zero-Shot Sentiment Classification by Natural Language Supervision](https://mlsanity.com/paper/2309.12056/) (2023)
- [WheelPose: Data Synthesis Techniques to Improve Pose Estimation Performance on Wheelchair Users](https://mlsanity.com/paper/2404.17063/) (2024)
- [Building Better Datasets: Seven Recommendations for Responsible Design from Dataset Creators](https://mlsanity.com/paper/2409.00252/) (2024)
- [Swing-by Dynamics in Concept Learning and Compositional Generalization](https://mlsanity.com/paper/2410.08309/) (2024)
- [SpeechWeave: Diverse Multilingual Synthetic Text & Audio Data Generation Pipeline for Training Text to Speech Models](https://mlsanity.com/paper/2509.14270/) (2025)
- [Quantifying Diversity of Thought: A Predictive Law of Weighted LLM Ensemble Lift](https://mlsanity.com/paper/2607.17384/) (2026)
- [Exploring Machine Teaching with Children](https://mlsanity.com/paper/2109.11434/) (2021)
- [Mutual Information Analysis in Multimodal Learning Systems](https://mlsanity.com/paper/2405.12456/) (2024)
- [MIME: Minority Inclusion for Majority Group Enhancement of AI Performance](https://mlsanity.com/paper/2209.00746/) (2022)
- [Optimization of Inter-group Criteria for Clustering with Minimum Size Constraints](https://mlsanity.com/paper/2401.07091/) (2024)
- [Metarobotics for Industry and Society: Vision, Technologies, and Opportunities](https://mlsanity.com/paper/2404.00797/) (2024)
- [Exploiting Frequency Correlation for Hyperspectral Image Reconstruction](https://mlsanity.com/paper/2406.00683/) (2024)
- [MUSS: Multilevel Subset Selection for Relevance and Diversity](https://mlsanity.com/paper/2503.11126/) (2025)
- [Simple Regularisation for Uncertainty-Aware Knowledge Distillation](https://mlsanity.com/paper/2205.09526/) (2022)
- [Explaining text classifiers through progressive neighborhood approximation with realistic samples](https://mlsanity.com/paper/2302.07733/) (2023)
- [Exploring Resiliency to Natural Image Corruptions in Deep Learning using Design Diversity](https://mlsanity.com/paper/2303.09283/) (2023)
- [Federated Learning Model Aggregation in Heterogenous Aerial and Space Networks](https://mlsanity.com/paper/2305.16351/) (2023)

## Abstract (arXiv)

Machine learning methods have achieved good performance and been widely applied in various real-world applications. They can learn the model adaptively and be better fit for special requirements of different tasks. Generally, a good machine learning system is composed of plentiful training data, a good model training process, and an accurate inference. Many factors can affect the performance of the machine learning process, among which the diversity of the machine learning process is an important one. The diversity can help each procedure to guarantee a total good machine learning: diversity of the training data ensures that the training data can provide more discriminative information for the model, diversity of the learned model (diversity in parameters of each model or diversity among different base models) makes each parameter/model capture unique or complement information and the diversity in inference can provide multiple choices each of which corresponds to a specific plausible local optimal result. Even though the diversity plays an important role in machine learning process, there is no systematical analysis of the diversification in machine learning system. In this paper, we systematically summarize the methods to make data diversification, model diversification, and inference diversification in the machine learning process, respectively. In addition, the typical applications where the diversity technology improved the machine learning performance have been surveyed, including the remote sensing imaging tasks, machine translation, camera relocalization, image segmentation, object detection, topic modeling, and others. Finally, we discuss some challenges of the diversity technology in machine learning and point out some directions in future work.