# Interpretable Compositional Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2107.04474 · HTML: https://mlsanity.com/paper/2107.04474/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 32 references that are in the map.
- First posted on arXiv 9 Jul 2021.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets](https://mlsanity.com/paper/1606.03657/) (2016)
- [This Looks Like That: Deep Learning for Interpretable Image Recognition](https://mlsanity.com/paper/1806.10574/) (2018)
- [Network Dissection: Quantifying Interpretability of Deep Visual Representations](https://mlsanity.com/paper/1704.05796/) (2017)
- [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017)
- [Detect What You Can: Detecting and Representing Objects using Holistic Models and Body Parts](https://mlsanity.com/paper/1406.2031/) (2014)
- [Interpretable Convolutional Neural Networks](https://mlsanity.com/paper/1710.00935/) (2017)
- [Net2Vec: Quantifying and Explaining how Concepts are Encoded by Filters in Deep Neural Networks](https://mlsanity.com/paper/1801.03454/) (2018)
- [Interpretable and Accurate Fine-grained Recognition via Region Grouping](https://mlsanity.com/paper/2005.10411/) (2020)
- [Compositional Convolutional Neural Networks: A Deep Architecture with Innate Robustness to Partial Occlusion](https://mlsanity.com/paper/2003.04490/) (2020)
- [Training Interpretable Convolutional Neural Networks by Differentiating Class-specific Filters](https://mlsanity.com/paper/2007.08194/) (2020)
- [Semantic Part Segmentation using Compositional Model combining Shape and Appearance](https://mlsanity.com/paper/1412.6124/) (2014)
- [Teaching Compositionality to CNNs](https://mlsanity.com/paper/1706.04313/) (2017)

## Built on by (most cited first, 16 of 16)

- [Quantifying the Knowledge in a DNN to Explain Knowledge Distillation for Classification](https://mlsanity.com/paper/2208.08741/) (2022)
- [A Comprehensive Survey on Self-Interpretable Neural Networks](https://mlsanity.com/paper/2501.15638/) (2025)
- [Don't Forget Too Much: Towards Machine Unlearning on Feature Level](https://mlsanity.com/paper/2406.10951/) (2024)
- [ME-D2N: Multi-Expert Domain Decompositional Network for Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2210.05280/) (2022)
- [Adversarial Attacks on the Interpretation of Neuron Activation Maximization](https://mlsanity.com/paper/2306.07397/) (2023)
- [A Large-Scale Empirical Study on Improving the Fairness of Image Classification Models](https://mlsanity.com/paper/2401.03695/) (2024)
- [Bort: Towards Explainable Neural Networks with Bounded Orthogonal Constraint](https://mlsanity.com/paper/2212.09062/) (2022)
- [Path Choice Matters for Clear Attribution in Path Methods](https://mlsanity.com/paper/2401.10442/) (2024)
- [Layerwise Change of Knowledge in Neural Networks](https://mlsanity.com/paper/2409.08712/) (2024)
- [PICNN: A Pathway towards Interpretable Convolutional Neural Networks](https://mlsanity.com/paper/2312.12068/) (2023)
- [Improving Network Interpretability via Explanation Consistency Evaluation](https://mlsanity.com/paper/2408.04600/) (2024)
- [A Survey on Interpretability in Visual Recognition](https://mlsanity.com/paper/2507.11099/) (2025)
- [Disentangling Visual Transformers: Patch-level Interpretability for Image Classification](https://mlsanity.com/paper/2502.17196/) (2025)
- [TCNL: Transparent and Controllable Network Learning Via Embedding Human-Guided Concepts](https://mlsanity.com/paper/2210.03274/) (2022)
- [DBAT: Dynamic Backward Attention Transformer for Material Segmentation with Cross-Resolution Patches](https://mlsanity.com/paper/2305.03919/) (2023)
- [Concept Labels Are Not Enough: Rethinking Concept Bottleneck Models through Representation Integrity](https://mlsanity.com/paper/2510.15770/) (2025)

## Abstract (arXiv)

The reasonable definition of semantic interpretability presents the core challenge in explainable AI. This paper proposes a method to modify a traditional convolutional neural network (CNN) into an interpretable compositional CNN, in order to learn filters that encode meaningful visual patterns in intermediate convolutional layers. In a compositional CNN, each filter is supposed to consistently represent a specific compositional object part or image region with a clear meaning. The compositional CNN learns from image labels for classification without any annotations of parts or regions for supervision. Our method can be broadly applied to different types of CNNs. Experiments have demonstrated the effectiveness of our method.