We present an unsupervised deep learning model for 3D object classification.
Conventional Hebbian learning, a well-known unsupervised model, suffers from
loss of local features leading to reduced performance for tasks with complex
geometric objects. We present a deep network with a novel Neuron Activity Aware
(NeAW) Hebbian learning rule that dynamically switches the neurons to be
governed by Hebbian learning or anti-Hebbian learning, depending on its
activity. We analytically show that NeAW Hebbian learning relieves the bias in
neuron activity, allowing more neurons to attend to the representation of the
3D objects. Empirical results show that the NeAW Hebbian learning outperforms
other variants of Hebbian learning and shows higher accuracy over fully
supervised models when training data is limited.

本文提出一种无监督的深度学习模型用于 3D 物体分类，该模型采用新颖的神经元活动感知 Hebbian 学习规则，可以动态地调整神经元的 Hebbian 学习或反 Hebbian 学习，以适应复杂几何物体的任务，并通过理论分析和实验结果证明该模型优于其他 Hebbian 学习模型并且在训练数据受限的情况下表现更好。