# Oil-Painting Style Classification Using ResNet with Conditional Information Bottleneck Regularization

## TL;DR

This paper introduces a new deep learning framework for classifying oil-painting styles using a conditional information bottleneck method with ResNet, improving accuracy and interpretability.

## Contribution

The novel contribution is the use of a conditional information bottleneck regularization with Rényi’s entropy estimator for oil-painting style classification.

## Key findings

- The proposed CIB framework achieves 13.1% and 11.9% relative performance gains on the Pandora and OilPainting datasets, respectively.
- The method produces disentangled latent representations that cluster semantically similar painting styles.

## Abstract

Automatic classification of oil-painting styles holds significant promise for art history, digital archiving, and forensic investigation by offering objective, scalable analysis of visual artistic attributes. In this paper, we introduce a deep conditional information bottleneck (CIB) framework, built atop ResNet-50, for fine-grained style classification of oil paintings. Unlike traditional information bottleneck (IB) approaches that minimize the mutual information I(X;Z) between input X and latent representation Z, our CIB minimizes the conditional mutual information I(X;Z∣Y), where Y denotes the painting’s style label. We implement this conditional term using a matrix-based Rényi’s entropy estimator, thereby avoiding costly variational approximations and ensuring computational efficiency. We evaluate our method on two public benchmarks: the Pandora dataset (7740 images across 12 artistic movements) and the OilPainting dataset (19,787 images across 17 styles). Our method outperforms the prevalent ResNet with a relative performance gain of 13.1% on Pandora and 11.9% on OilPainting. Beyond quantitative gains, our approach yields more disentangled latent representations that cluster semantically similar styles, facilitating interpretability.