# ARTxAI: Explainable Artificial Intelligence Curates Deep Representation Learning for Artistic Images using Fuzzy Techniques arXiv: https://arxiv.org/abs/2308.15284 ยท HTML: https://mlsanity.com/paper/2308.15284/ ## Key facts - Cited by 10 papers in the map (data through 14 Oct 2026). - Builds on 24 of its 73 references that are in the map. - First posted on arXiv 29 Aug 2023. ## Builds on (most cited first, 24 of 24) - [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021) - [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020) - [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014) - [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016) - [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022) - [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016) - [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019) - [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019) - [Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right Feature](https://mlsanity.com/paper/1505.00855/) (2015) - [Variational Autoencoder for Deep Learning of Images, Labels and Captions](https://mlsanity.com/paper/1609.08976/) (2016) - [Resnet in Resnet: Generalizing Residual Architectures](https://mlsanity.com/paper/1603.08029/) (2016) - [Medical Image Analysis using Convolutional Neural Networks: A Review](https://mlsanity.com/paper/1709.02250/) (2017) - [How to Read Paintings: Semantic Art Understanding with Multi-Modal Retrieval](https://mlsanity.com/paper/1810.09617/) (2018) - [CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification](https://mlsanity.com/paper/2204.14244/) (2022) - [The Shape of Art History in the Eyes of the Machine](https://mlsanity.com/paper/1801.07729/) (2018) - [Weakly Supervised Object Detection in Artworks](https://mlsanity.com/paper/1810.02569/) (2018) - [Context-Aware Embeddings for Automatic Art Analysis](https://mlsanity.com/paper/1904.04985/) (2019) - [Failure Modes of Variational Autoencoders and Their Effects on Downstream Tasks](https://mlsanity.com/paper/2007.07124/) (2020) - [GCNBoost: Artwork Classification by Label Propagation through a Knowledge Graph](https://mlsanity.com/paper/2105.11852/) (2021) - [A Temporal Type-2 Fuzzy System for Time-dependent Explainable Artificial Intelligence](https://mlsanity.com/paper/2210.12571/) (2022) - [Automatic analysis of artistic paintings using information-based measures](https://mlsanity.com/paper/2102.01767/) (2021) - [How Well Do Vision Transformers (VTs) Transfer To The Non-Natural Image Domain? An Empirical Study Involving Art Classification](https://mlsanity.com/paper/2208.04693/) (2022) - [Community detection and Social Network analysis based on the Italian wars of the 15th century](https://mlsanity.com/paper/2007.02641/) (2020) - [Integrating Contextual Knowledge to Visual Features for Fine Art Classification](https://mlsanity.com/paper/2105.15028/) (2021) ## Built on by (most cited first, 10 of 10) - [Enhancing Concept Localization in CLIP-based Concept Bottleneck Models](https://mlsanity.com/paper/2510.07115/) (2025) - [Efficient Learning of Fuzzy Logic Systems for Large-Scale Data Using Deep Learning](https://mlsanity.com/paper/2404.12792/) (2024) - [Artificial Intelligence for Geometry-Based Feature Extraction, Analysis and Synthesis in Artistic Images: A Survey](https://mlsanity.com/paper/2412.01450/) (2024) - [Crisp complexity of fuzzy classifiers](https://mlsanity.com/paper/2504.15791/) (2025) - [A Fast Interpretable Fuzzy Tree Learner](https://mlsanity.com/paper/2512.11616/) (2025) - [Interpreting Contrastive Embeddings in Specific Domains with Fuzzy Rules](https://mlsanity.com/paper/2603.12227/) (2026) - [Assessing Reliability of Symbol Detection in Concept Bottleneck Models](https://mlsanity.com/paper/2606.16535/) (2026) - [Factor-Informed Uncertainty Distillation for Gaze Estimation](https://mlsanity.com/paper/2607.20072/) (2026) - [Interpretable Fuzzy Rule-Based Regression Extension for Ex-Fuzzy Library](https://mlsanity.com/paper/2607.20277/) (2026) - [A Framework for Reputation Aware Uninorm-driven Consensus Algorithms for Blockchain Networks](https://mlsanity.com/paper/2607.20700/) (2026) ## Abstract (arXiv) Automatic art analysis employs different image processing techniques to classify and categorize works of art. When working with artistic images, we need to take into account further considerations compared to classical image processing. This is because such artistic paintings change drastically depending on the author, the scene depicted, and their artistic style. This can result in features that perform very well in a given task but do not grasp the whole of the visual and symbolic information contained in a painting. In this paper, we show how the features obtained from different tasks in artistic image classification are suitable to solve other ones of similar nature. We present different methods to improve the generalization capabilities and performance of artistic classification systems. Furthermore, we propose an explainable artificial intelligence method to map known visual traits of an image with the features used by the deep learning model considering fuzzy rules. These rules show the patterns and variables that are relevant to solve each task and how effective is each of the patterns found. Our results show that our proposed context-aware features can achieve up to 6% and 26% more accurate results than other context- and non-context-aware solutions, respectively, depending on the specific task. We also show that some of the features used by these models can be more clearly correlated to visual traits in the original image than others.