In this paper, we introduce the new ideas of augmenting Convolutional Neural Networks (CNNs) with Memory and learning to learn the network parameters for the unlabelled images on the fly in one-shot learning. Specifically, we present Memory Matching Networks (MM-Net) - a novel deep architecture that explores the training procedure, following the philosophy that training and test conditions must match. Technically, MM-Net writes the features of a set of labelled images (support set) into memory and reads from memory when performing inference to holistically leverage the knowledge in the set. Meanwhile, a Contextual Learner employs the memory slots in a sequential manner to predict the parameters of CNNs for unlabelled images. The whole architecture is trained by once showing only a few examples per class and switching the learning from minibatch to minibatch, which is tailored for one-shot learning when presented with a few examples of new categories at test time. Unlike the conventional one-shot learning approaches, our MM-Net could output one unified model irrespective of the number of shots and categories. Extensive experiments are conducted on two public datasets, i.e., Omniglot and miniImageNet, and superior results are reported when compared to state-of-the-art approaches. More remarkably, our MM-Net improves one-shot accuracy on Omniglot from 98.95 to 99.28 and from 49.21 to 53.37 on miniImageNet.
In summary, our work belongs to deep embedding learning method for one-shot learning. However, most of the above methods in this direction mainly focus on forming the deep embedding space with the simple objective of matching-based classification (i.e., to maximize the matching score between unlabelled image and the support images with the same label). Our work is different that we enhance the one-shot learning by leveraging memory module to additionally integrate the contextual information across support samples into the deep embedding architectures. It is worth noting that @cite_24 also involves contextual information for one-shot learning. Ours is fundamentally different in the way that all the CNNs in @cite_24 need to be learnt at training stage, as opposed to directly predicting the parameters of CNNs for unlabelled image based on the contextual information encoded in the memory slots of this work, which is better-suited for one-shot learning during inference on unseen categories.
@cite_24
2963341924
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.