# Incremental Concept Formation over Visual Images Without Catastrophic Forgetting

arXiv: https://arxiv.org/abs/2402.16933 · HTML: https://mlsanity.com/paper/2402.16933/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 44 references that are in the map.
- First posted on arXiv 26 Feb 2024.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [Progressive Neural Networks](https://mlsanity.com/paper/1606.04671/) (2016)
- [On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima](https://mlsanity.com/paper/1609.04836/) (2016)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Continual Learning with Deep Generative Replay](https://mlsanity.com/paper/1705.08690/) (2017)
- [A Comprehensive Survey of Continual Learning: Theory, Method and Application](https://mlsanity.com/paper/2302.00487/) (2023)
- [Three scenarios for continual learning](https://mlsanity.com/paper/1904.07734/) (2019)
- [Progress & Compress: A scalable framework for continual learning](https://mlsanity.com/paper/1805.06370/) (2018)
- [Recent Advances in Convolutional Neural Networks](https://mlsanity.com/paper/1512.07108/) (2015)
- [Convolutional Cobweb: A Model of Incremental Learning from 2D Images](https://mlsanity.com/paper/2201.06740/) (2022)
- [Efficient Induction of Language Models Via Probabilistic Concept Formation](https://mlsanity.com/paper/2212.11937/) (2022)

## Built on by (most cited first, 7 of 7)

- [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)
- [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)
- [Diffusion Models and Concept Formation](https://mlsanity.com/paper/2609.13047/) (2026)

## Abstract (arXiv)

Deep neural networks have excelled in machine learning, particularly in vision tasks, however, they often suffer from catastrophic forgetting when learning new tasks sequentially. In this work, we introduce Cobweb4V, an alternative to traditional neural network approaches. Cobweb4V is a novel visual classification method that builds on Cobweb, a human like learning system that is inspired by the way humans incrementally learn new concepts over time. In this research, we conduct a comprehensive evaluation, showcasing Cobweb4Vs proficiency in learning visual concepts, requiring less data to achieve effective learning outcomes compared to traditional methods, maintaining stable performance over time, and achieving commendable asymptotic behavior, without catastrophic forgetting effects. These characteristics align with learning strategies in human cognition, positioning Cobweb4V as a promising alternative to neural network approaches.