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Initial attempts on one-shot learning in computer vision domain are based on probabilistic approaches. Fei-Fei et al. [4] in 2003, have introduced a model to learn visual concepts and then use that knowledge to learn new categories. They have used a variational Bayesian framework. Here, the probabilistic models have used to represent the object groups and a probability density function has used to denote the prior knowledge. Their model has supported to learn four visual concepts, human faces, aeroplanes, motorcycles, and spotted cats. Initially, abstract knowledge is learned by training on many samples belong to three categories. Then this knowledge is used to understand the remaining category with the help of a small number of examples (1 to 5 training examples).

Lately, neural networks came in as a solution to the one-shot learning problem. The two main types of networks used in the one-shot learning tasks are memory augmented neural networks [26, 27] and Siamese neural networks [7, 24, 28]. Memory augmented neural networks are similar to Recurrent neural networks (RNN), but they have an external memory and try to separate the computation from memory [29]. Siamese networks have two similar branches of networks, and the output of those compared to get a decision on one-shot task. Most of the time, Siamese network branches are built on convolutional layers or fully connected layers.