# Concept-Oriented Deep Learning

arXiv: https://arxiv.org/abs/1806.01756 · HTML: https://mlsanity.com/paper/1806.01756/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2018.

## Built on by (most cited first, 5 of 5)

- [A survey on Concept-based Approaches For Model Improvement](https://mlsanity.com/paper/2403.14566/) (2024)
- [Concept Representation Learning with Contrastive Self-Supervised Learning](https://mlsanity.com/paper/2112.05677/) (2021)
- [Exemplar-Based Contrastive Self-Supervised Learning with Few-Shot Class Incremental Learning](https://mlsanity.com/paper/2202.02601/) (2022)
- [Dual Embodied-Symbolic Concept Representations for Deep Learning](https://mlsanity.com/paper/2203.00600/) (2022)
- [Concept-Oriented Deep Learning with Large Language Models](https://mlsanity.com/paper/2306.17089/) (2023)

## Abstract (arXiv)

Concepts are the foundation of human deep learning, understanding, and knowledge integration and transfer. We propose concept-oriented deep learning (CODL) which extends (machine) deep learning with concept representations and conceptual understanding capability. CODL addresses some of the major limitations of deep learning: interpretability, transferability, contextual adaptation, and requirement for lots of labeled training data. We discuss the major aspects of CODL including concept graph, concept representations, concept exemplars, and concept representation learning systems supporting incremental and continual learning.