Matching Networks for One Shot Learning
Learning from a few examples remains a key challenge in machine learning. Despite recent advances in important domains such as vision and language, the standard supervised deep learning paradigm does not offer a satisfactory solution for learning new concepts rapidly from little data. In this work, we employ ideas from metric learning based on deep neural features and from recent advances that augment neural networks with external memories. Our framework learns a network that maps a small labelled support set and an unlabelled example to its label, obviating the need for fine-tuning to adapt to new class types. We then define one-shot learning problems on vision (using Omniglot, ImageNet) and language tasks. Our algorithm improves one-shot accuracy on ImageNet from 87.6% to 93.2% and from 88.0% to 93.8% on Omniglot compared to competing approaches. We also demonstrate the usefulness of the same model on language modeling by introducing a one-shot task on the Penn Treebank.
The article describes a one-shot learning model called matching network. The basic idea of the model is to compute the output category of one given test example by computing a kind of ‘similarity’ with all the training examples that are stored in a memory. This similarity (denoted a(x,x_i)) is based on two representation functions f and g which goals are to encode both the new example, but also the examples stored in memory. The proposed model is thus very close to siamese networks, but using an asymmetric architectures since g and f can be different functions. The main originality is contained in two particular aspects: first, instead of computing the g function on each training example, the model proposes a sequential model (bidirectional LSTM) to learn to encode the training examples based on the previously seen examples. Second, the set of examples is also used to learn to encode any incoming datapoint. These two components will be learned over a set of one-shot learning tasks, resulting in a ‘learning to learn’ problem. Experiments are made on different images datasets and one language dataset. The proposed approach is compared to baseline models, MANN and siamese nets (not on all the datasets…) and shows interesting results
Comments: the idea contained in the paper is not a very big contribution to the field but still remains interesting, and the experiments on different datasets provide important information concerning the behavior of the model. But the paper has many different problems that make it difficult to understand. The first problem is in the structure of the paper since the problem formulation is in fact only given in Section 2.2 while I think it is important to well define the one-shot learning problem at the beginning of the paper. It would make the article easier to read and to follow. Sections  2.1 and 2.1.1 are well written but could include more precise definitions. For example, in Equation 1, y_i is not defined : is it a real number, a one hot vector (I suppose it is a one hot vector). The core of the contributions in Section 2.1.2 is very difficult to well understand. It is not clear how the bidirectional LSTM is used, but the more difficult part concerns the way f(\hat(x)) is defined. The authors could clearly give more details since this section is in fact the main contribution of the article. Written as it is, it makes the model difficult to understand, and almost impossible to reproduce. At last, if these sections can be understood by researchers very familiar to deep learning, it will not be the case for ML researchers used to read more ‘classical’ papers while the principle of the paper is more general than deep learning and can be for example understood from a metric learning point of view. I clearly advise the authors to improve the way the model is described. Concerning the experiments, it is really interesting to see that this type of approach improves the quality of the system w.r.t Siamese networks. But the Siamese net results are only provided for omniglot and not for imagenet which is a little bit strange. The language model part also does not contain any table/figure showing a summary of the results which make the discourse difficult to follow in this section.   To conclude, if the article provides a simple but interesting idea for one-shot learning, the way it is written must be drastically improved to allow a publication in a top conference.