论文标题
在有16个神经元的路上：具有生物启发的深神经网络的心理图像
On the Road with 16 Neurons: Mental Imagery with Bio-inspired Deep Neural Networks
论文作者
论文摘要
本文提出了一种在自主驾驶背景下进行视觉预测的策略。人类在不分心或醉酒时仍然是您目前可以找到的最好的驾驶员。因此，我们从关于人类思想及其神经组织的两个理论思想中汲取灵感。第一个想法涉及大脑如何使用神经合奏的层次结构来从视觉体验中提取抽象概念并将其编码为紧凑的表示。第二个想法表明，这些神经感知表征不是中立的，而是对环境中未来状况的预测起作用。同样，预测机制不是中立的，而是针对未来行动的当前计划。我们在深度学习框架内确定了上述神经认知理论的两个人工对应物。我们发现第一个理论思想与卷积自动编码器的体系结构之间的对应关系，而我们将第二个理论转化为一个训练程序，该过程从两个不同的角度学习了不中立但面向驱动任务的紧凑表示形式。从静态的角度来看，我们强迫紧凑型表示中的神经单位组明显地表示对驾驶任务至关重要的特定概念。从动态的角度来看，我们鼓励紧凑型表示可以预测当前的道路情景将来将如何变化。我们成功地学习了紧凑的表示，对于我们考虑的两个基本驾驶概念中的每个概念中的每个概念：汽车和车道。我们证明了我们在合成数据集上提出的感知表示的效率。我们的源代码可从https://github.com/3lis/rnn_vae获得
This paper proposes a strategy for visual prediction in the context of autonomous driving. Humans, when not distracted or drunk, are still the best drivers you can currently find. For this reason we take inspiration from two theoretical ideas about the human mind and its neural organization. The first idea concerns how the brain uses a hierarchical structure of neuron ensembles to extract abstract concepts from visual experience and code them into compact representations. The second idea suggests that these neural perceptual representations are not neutral but functional to the prediction of the future state of affairs in the environment. Similarly, the prediction mechanism is not neutral but oriented to the current planning of a future action. We identify within the deep learning framework two artificial counterparts of the aforementioned neurocognitive theories. We find a correspondence between the first theoretical idea and the architecture of convolutional autoencoders, while we translate the second theory into a training procedure that learns compact representations which are not neutral but oriented to driving tasks, from two distinct perspectives. From a static perspective, we force groups of neural units in the compact representations to distinctly represent specific concepts crucial to the driving task. From a dynamic perspective, we encourage the compact representations to be predictive of how the current road scenario will change in the future. We successfully learn compact representations that use as few as 16 neural units for each of the two basic driving concepts we consider: car and lane. We prove the efficiency of our proposed perceptual representations on the SYNTHIA dataset. Our source code is available at https://github.com/3lis/rnn_vae
