Learning a new concept from one example is a superior function of human brain and it is drawing attention in the field of machine learning as one-shot learning task. In this paper, we propose the simplest method for this task, named Direct ONE-shot learning (DONE). DONE adds a new class to a pretrained deep neural network (DNN) classifier with neither training optimization nor other-classes modification. DONE is inspired by Hebbian theory and directly uses the neural activity input of the final dense layer obtained from a data that belongs to the new additional class as the connectivity weight (synaptic strength) with a newly-provided-output neuron for the new class. DONE requires just one inference for obtaining the output of the final dense layer and its procedure is simple, deterministic, not requiring parameter tuning and hyperparameters. The performance of DONE depends entirely on the pretrained DNN model used as a backbone model, and we confirmed that DONE with a well-trained backbone model performs a practical-level accuracy. DONE has some advantages including a DNN's practical use that is difficult to spend high cost for a training, an evaluation of existing DNN models, and the understanding of the brain. DONE might be telling us one-shot learning is an easy task that can be achieved by a simple principle not only for humans but also for current well-trained DNN models.
翻译:从一个实例中学习一个新概念是人类大脑的优越功能,它正在机器学习领域引起人们的注意,作为一次性学习任务。在本文中,我们提出这一任务的最简单方法,名为直接一光学习(DONE)。DONE在预先培训的深神经网络分类器(DNN)中增加了一个新的类别,既不要求优化培训,也不要求其它等级的修改。DONE完全依赖Hebbian理论的启发,并直接使用从属于新类别的数据中获取的最后密集层神经活动输入神经素,该数据属于新的类别,例如连接权重(合成力),具有新类别中新提供的输出神经元。DONE需要为获得最后密集层的输出提供一种推论,其程序简单、确定性,不需要参数调整和超分度计。DONE的性能完全取决于预先培训的DNNM模型,我们确认,只有经过良好训练的骨干模型才能进行实际程度的精确性。DONE有一些好处,包括DNNNE的实际用途,但这种实际用途对于最终的大脑模型的学习成本可能非常简单。D,而我们目前的一项任务需要一个容易的学习的模型。DNNNNNE模型,一个简单的模型。