  Painting classification plays a vital role in organizing, finding, and
suggesting artwork for digital and classic art galleries. Existing methods
struggle with adapting knowledge from the real world to artistic images during
training, leading to poor performance when dealing with different datasets. Our
innovation lies in addressing these challenges through a two-step process.
First, we generate more data using Style Transfer with Adaptive Instance
Normalization (AdaIN), bridging the gap between diverse styles. Then, our
classifier gains a boost with feature-map adaptive spatial attention modules,
improving its understanding of artistic details. Moreover, we tackle the
problem of imbalanced class representation by dynamically adjusting augmented
samples. Through a dual-stage process involving careful hyperparameter search
and model fine-tuning, we achieve an impressive 87.24\% accuracy using the
ResNet-50 backbone over 40 training epochs. Our study explores quantitative
analyses that compare different pretrained backbones, investigates model
optimization through ablation studies, and examines how varying augmentation
levels affect model performance. Complementing this, our qualitative
experiments offer valuable insights into the model's decision-making process
using spatial attention and its ability to differentiate between easy and
challenging samples based on confidence ranking.
