@inproceedings{
sharafeldin2023active,
title={Active Vision with Predictive Coding and Uncertainty Minimization},
author={Abdelrahman Sharafeldin and Nabil Imam and Hannah Choi},
booktitle={NeurIPS 2023 workshop: Information-Theoretic Principles in Cognitive Systems},
year={2023},
url={https://openreview.net/forum?id=PfdpAPRxf5}
}
We present an end-to-end procedure for embodied visual exploration based on two biologically inspired computations: predictive coding and uncertainty minimization. The procedure can be applied in a task-independent and intrinsically driven manner. We evaluate our approach on an active vision task, where an agent actively samples its visual environment to gather information. We show that our model builds unsupervised representations through exploration that allow it to efficiently categorize visual scenes. We further show that using these representations for downstream classification leads to superior data efficiency and learning speed compared to other baselines while maintaining lower parameter complexity.  Finally, the modularity of our model allows us to probe its internal mechanisms and analyze the interaction between perception and action during exploratory behavior.
Active Vision with Predictive Coding and Uncertainty Minimization
Abdelrahman Sharafeldin Nabil Imam Hannah Choi