# Convolutional Cobweb: A Model of Incremental Learning from 2D Images

arXiv: https://arxiv.org/abs/2201.06740 · HTML: https://mlsanity.com/paper/2201.06740/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 18 Jan 2022.

## Builds on (most cited first, 2 of 2)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RMDL: Random Multimodel Deep Learning for Classification](https://mlsanity.com/paper/1805.01890/) (2018)

## Built on by (most cited first, 10 of 10)

- [Efficient Induction of Language Models Via Probabilistic Concept Formation](https://mlsanity.com/paper/2212.11937/) (2022)
- [Incremental Concept Formation over Visual Images Without Catastrophic Forgetting](https://mlsanity.com/paper/2402.16933/) (2024)
- [Cobweb: An Incremental and Hierarchical Model of Human-Like Category Learning](https://mlsanity.com/paper/2403.03835/) (2024)
- [Taxonomic Networks: A Representation for Neuro-Symbolic Pairing](https://mlsanity.com/paper/2505.24601/) (2025)
- [Deep Taxonomic Networks for Unsupervised Hierarchical Prototype Discovery](https://mlsanity.com/paper/2509.23602/) (2025)
- [Hierarchical Semantic Retrieval with Cobweb](https://mlsanity.com/paper/2510.02539/) (2025)
- [Incremental and Data-Efficient Concept Formation to Support Masked Word Prediction](https://mlsanity.com/paper/2409.12440/) (2024)
- [Explaining Robustness to Catastrophic Forgetting Through Incremental Concept Formation](https://mlsanity.com/paper/2510.23756/) (2025)
- [CobwebTM: Probabilistic Concept Formation for Lifelong and Hierarchical Topic Modeling](https://mlsanity.com/paper/2604.14489/) (2026)
- [A Unified Account of Concepts and Chunks](https://mlsanity.com/paper/2609.30414/) (2026)

## Abstract (arXiv)

This paper presents a new concept formation approach that supports the ability to incrementally learn and predict labels for visual images. This work integrates the idea of convolutional image processing, from computer vision research, with a concept formation approach that is based on psychological studies of how humans incrementally form and use concepts. We experimentally evaluate this new approach by applying it to an incremental variation of the MNIST digit recognition task. We compare its performance to Cobweb, a concept formation approach that does not support convolutional processing, as well as two convolutional neural networks that vary in the complexity of their convolutional processing. This work represents a first step towards unifying modern computer vision ideas with classical concept formation research.