We compared the efficiency of the FlyHash model, an insect-inspired sparse neural network (Dasgupta et al., 2017), to similar but non-sparse models in an embodied navigation task. This requires a model to control steering by comparing current visual inputs to memories stored along a training route. We concluded the FlyHash model is more efficient than others, especially in terms of data encoding.
翻译:我们比较了FlyHash模型的效率,FlyHash模型是昆虫激发的稀有神经网络(Dasgupta等人,2017年),在包含的导航任务中,它与类似但非粗糙的模型相比。 这就需要一个模型,通过将当前视觉输入与沿训练路线存储的记忆进行比较来控制方向。 我们得出结论,FlyHash模型比其他模型更有效,特别是在数据编码方面。</s>