论文标题
复发性神经回路用于轮廓检测
Recurrent neural circuits for contour detection
论文作者
论文摘要
我们介绍了一个深层复发的神经网络结构，该结构近似于视觉皮层电路。我们表明，我们称之为伽马网络的建筑学会了与最先进的前馈网络相比，以更好的样品效率来解决轮廓检测任务，同时也表现出经典的感知幻觉，称为方向倾斜。纠正这种幻觉可显着降低伽马网状轮廓检测精度，而偏爱低级边缘比高级对象边界轮廓。总体而言，我们的研究表明，方向倾斜的幻觉是神经回路的副产品，可帮助生物学视觉系统实现强大而有效的轮廓检测，并且将这些电路纳入人工神经网络可以改善计算机视觉。
We introduce a deep recurrent neural network architecture that approximates visual cortical circuits. We show that this architecture, which we refer to as the gamma-net, learns to solve contour detection tasks with better sample efficiency than state-of-the-art feedforward networks, while also exhibiting a classic perceptual illusion, known as the orientation-tilt illusion. Correcting this illusion significantly reduces gamma-net contour detection accuracy by driving it to prefer low-level edges over high-level object boundary contours. Overall, our study suggests that the orientation-tilt illusion is a byproduct of neural circuits that help biological visual systems achieve robust and efficient contour detection, and that incorporating these circuits in artificial neural networks can improve computer vision.
