# EPIC: Explanation of Pretrained Image Classification Networks via Prototype

arXiv: https://arxiv.org/abs/2505.12897 · HTML: https://mlsanity.com/paper/2505.12897/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 22 references that are in the map.
- First posted on arXiv 19 May 2025.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Sanity Checks for Saliency Maps](https://mlsanity.com/paper/1810.03292/) (2018)
- [RISE: Randomized Input Sampling for Explanation of Black-box Models](https://mlsanity.com/paper/1806.07421/) (2018)
- [This Looks Like That: Deep Learning for Interpretable Image Recognition](https://mlsanity.com/paper/1806.10574/) (2018)
- [Not Just a Black Box: Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1605.01713/) (2016)
- [Concept Whitening for Interpretable Image Recognition](https://mlsanity.com/paper/2002.01650/) (2020)
- [B-cos Networks: Alignment is All We Need for Interpretability](https://mlsanity.com/paper/2205.10268/) (2022)
- [FunnyBirds: A Synthetic Vision Dataset for a Part-Based Analysis of Explainable AI Methods](https://mlsanity.com/paper/2308.06248/) (2023)
- [Fast Axiomatic Attribution for Neural Networks](https://mlsanity.com/paper/2111.07668/) (2021)
- [InfoDisent: Explainability of Image Classification Models by Information Disentanglement](https://mlsanity.com/paper/2409.10329/) (2024)

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

- [SIDE: Sparse Information Disentanglement for Explainable Artificial Intelligence](https://mlsanity.com/paper/2507.19321/) (2025)
- [ProtoQuant: Quantization of Prototypical Parts For General and Fine-Grained Image Classification](https://mlsanity.com/paper/2602.06592/) (2026)
- [XSPLAIN: XAI-enabling Splat-based Prototype Learning for Attribute-aware INterpretability](https://mlsanity.com/paper/2602.10239/) (2026)
- [ProDG: Prototypes for Data-Free Generative Post-Hoc Explainability](https://mlsanity.com/paper/2605.08858/) (2026)
- [APEX: Audio Prototype EXplanations for Classification Tasks](https://mlsanity.com/paper/2605.10153/) (2026)
- [Conceptualizing Embeddings: Sparse Disentanglement for Vision-Language Models](https://mlsanity.com/paper/2605.22679/) (2026)
- [OPAL: Orthonormal Prototype Alignment Learning for Interpretable Image Classification](https://mlsanity.com/paper/2608.30003/) (2026)

## Abstract (arXiv)

Explainable AI (XAI) methods generally fall into two categories. Post-hoc approaches generate explanations for pre-trained models and are compatible with various neural network architectures. These methods often use feature importance visualizations, such as saliency maps, to indicate which input regions influenced the model's prediction. Unfortunately, they typically offer a coarse understanding of the model's decision-making process. In contrast, ante-hoc (inherently explainable) methods rely on specially designed model architectures trained from scratch. A notable subclass of these methods provides explanations through prototypes, representative patches extracted from the training data. However, prototype-based approaches have limitations: they require dedicated architectures, involve specialized training procedures, and perform well only on specific datasets. In this work, we propose EPIC (Explanation of Pretrained Image Classification), a novel approach that bridges the gap between these two paradigms. Like post-hoc methods, EPIC operates on pre-trained models without architectural modifications. Simultaneously, it delivers intuitive, prototype-based explanations inspired by ante-hoc techniques. To the best of our knowledge, EPIC is the first post-hoc method capable of fully replicating the core explanatory power of inherently interpretable models. We evaluate EPIC on benchmark datasets commonly used in prototype-based explanations, such as CUB-200-2011 and Stanford Cars, alongside large-scale datasets like ImageNet, typically employed by post-hoc methods. EPIC uses prototypes to explain model decisions, providing a flexible and easy-to-understand tool for creating clear, high-quality explanations.