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 模型与其他非稀疏模型在一个具有体现式导航任务中的效率。该任务要求模型通过将当前视觉输入与沿途训练路线上存储的记忆进行比较来控制转向。我们得出结论:与其他方法相比,FlyHash 模型在数据编码方面更加高效。