Landscape Of Neural Architecture Search Across Sensors How Much Do They Differ

# Landscape of Neural Architecture Search across sensors: how much do they differ ?

**arXiv ID:** 2201.06321
**Authors:** Kalifou René Traoré, Andrés Camero, Xiao Xiang Zhu
**Published:** 2022-01-17T10:14:39Z
**Abstract:**
With the rapid rise of neural architecture search, the ability to understand its complexity from the perspective of a search algorithm is desirable. Recently, Traoré et al. have proposed the framework of Fitness Landscape Footprint to help describe and compare neural architecture search problems. It attempts at describing why a search strategy might be successful, struggle or fail on a target task. Our study leverages this methodology in the context of searching across sensors, including sensor data fusion. In particular, we apply the Fitness Landscape Footprint to the real-world image classification problem of So2Sat LCZ42, in order to identify the most beneficial sensor to our neural network hyper-parameter optimization problem. From the perspective of distributions of fitness, our findings indicate a similar behaviour of the search space for all sensors: the longer the training time, the larger the overall fitness, and more flatness in the landscapes (less ruggedness and deviation). Regarding sensors, the better the fitness they enable (Sentinel-2), the better the search trajectories (smoother, higher persistence). Results also indicate very similar search behaviour for sensors that can be decently fitted by the search space (Sentinel-2 and fusion).

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## References

- [arXiv:2201.06321](http://arxiv.org/abs/2201.06321v2)