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The deployment of autonomous agents in real-world scenarios is challenged by"unknown unknowns", i.e. novel unexpected environments not encountered duringtraining, such as degraded signs. While existing research focuses on anomalydetection and class imbalance, it often fails to address truly novel scenarios.Our approach enhances visual perception by leveraging the VariationalPrototyping Encoder (VPE) to adeptly identify and handle novel inputs, thenincrementally augmenting data using neural style transfer to enrichunderrepresented data. By comparing models trained solely on original datasetswith those trained on a combination of original and augmented datasets, weobserved a notable improvement in the performance of the latter. Thisunderscores the critical role of data augmentation in enhancing modelrobustness. Our findings suggest the potential benefits of incorporatinggenerative models for domain-specific augmentation strategies.

Enhancing Visual Perception in Novel Environments via Incremental Data Augmentation Based on Style Transfer