# Haphazard Inputs as Images in Online Learning

arXiv: https://arxiv.org/abs/2504.02912 · HTML: https://mlsanity.com/paper/2504.02912/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 44 references that are in the map.
- First posted on arXiv 3 Apr 2025.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Time Series as Images: Vision Transformer for Irregularly Sampled Time Series](https://mlsanity.com/paper/2303.12799/) (2023)
- [Contributions of Shape, Texture, and Color in Visual Recognition](https://mlsanity.com/paper/2207.09510/) (2022)
- [Learning with Feature Evolvable Streams](https://mlsanity.com/paper/1706.05259/) (2017)
- [One-Pass Learning with Incremental and Decremental Features](https://mlsanity.com/paper/1605.09082/) (2016)
- [Prediction with Unpredictable Feature Evolution](https://mlsanity.com/paper/1904.12171/) (2019)
- [Auxiliary Network: Scalable and agile online learning for dynamic system with inconsistently available inputs](https://mlsanity.com/paper/2008.11828/) (2020)
- [Aux-Drop: Handling Haphazard Inputs in Online Learning Using Auxiliary Dropouts](https://mlsanity.com/paper/2303.05155/) (2023)
- [Online Learning under Haphazard Input Conditions: A Comprehensive Review and Analysis](https://mlsanity.com/paper/2404.04903/) (2024)

## Abstract (arXiv)

The field of varying feature space in online learning settings, also known as haphazard inputs, is very prominent nowadays due to its applicability in various fields. However, the current solutions to haphazard inputs are model-dependent and cannot benefit from the existing advanced deep-learning methods, which necessitate inputs of fixed dimensions. Therefore, we propose to transform the varying feature space in an online learning setting to a fixed-dimension image representation on the fly. This simple yet novel approach is model-agnostic, allowing any vision-based models to be applicable for haphazard inputs, as demonstrated using ResNet and ViT. The image representation handles the inconsistent input data seamlessly, making our proposed approach scalable and robust. We show the efficacy of our method on four publicly available datasets. The code is available at https://github.com/Rohit102497/HaphazardInputsAsImages.