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About this paper
Mark Sandler, Andrey Zhmoginov, Liangcheng Luo, Alexander Mordvintsev, Ettore Randazzo, Blaise Agúera y Arcas
A method to train cellular automata (CA) using gradient descent is introduced for image segmentation, enabling local update rules to learn complex global behaviors. This approach, incorporating techniques like mini-unrolls and reset gates, achieves proficient segmentation of high-resolution images with compact models and demonstrates robust adaptation to dynamic inputs over hundreds of steps.
Key insights
- Learned cellular automata can effectively perform context-aware image segmentation, demonstrating that complex vision tasks can be solved through iterative, purely local interactions.
- Techniques like mini-unrolls combined with reset gates are crucial for stabilizing CA training over long durations and enabling robust adaptation to dynamic inputs, like changing images.
- Compact CA models (e.g., ~9K parameters) can achieve competitive segmentation performance, offering advantages for resource-constrained environments and showcasing efficiency.
Summary by alphaXiv.
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