We train a network to generate mappings between training sets and classification policies (a 'classifier generator') by conditioning on the entire training set via an attentional mechanism. The network is directly optimized for test set performance on an training set of related tasks, which is then transferred to unseen 'test' tasks. We use this to optimize for performance in the low-data and unsupervised learning regimes, and obtain significantly better performance in the 10-50 datapoint regime than support vector classifiers, random forests, XGBoost, and k-nearest neighbors on a range of small datasets.
Metric-based meta-learning approaches have also been applied to the general problem of one-shot learning, with most of the results being in the image domain. This is a family of approaches which use a combination of an embedding transformation with interpretation of the embeddings in the context of an imposed metric space in order to achieve one-shot learning of similarity relationships in a shared domain (e.g. visual similarity). These approaches include matching networks @cite_31 , siamese networks @cite_40 , and FaceNet @cite_25 . The work Towards a Neural Statistician'' @cite_10 learns latent spaces which explain the statistics of the input (it extracts summary statistics from domain examples), which naturally produces a space that has good properties for metric-based one-shot learning approaches on datasets.
@cite_40 @cite_31 @cite_10 @cite_25
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We study few-shot learning in natural language domains. Compared to many existing works that apply either metric-based or optimization-based meta-learning to image domain with low inter-task variance, we consider a more realistic setting, where tasks are diverse. However, it imposes tremendous difficulties to existing state-of-the-art metric-based algorithms since a single metric is insufficient to capture complex task variations in natural language domain. To alleviate the problem, we propose an adaptive metric learning approach that automatically determines the best weighted combination from a set of metrics obtained from meta-training tasks for a newly seen few-shot task. Extensive quantitative evaluations on real-world sentiment analysis and dialog intent classification datasets demonstrate that the proposed method performs favorably against state-of-the-art few shot learning algorithms in terms of predictive accuracy. We make our code and data available for further study. A recent approach to few-shot classification called matching networks has demonstrated the benefits of coupling metric learning with a training procedure that mimics test. This approach relies on a complicated fine-tuning procedure and an attention scheme that forms a distribution over all points in the support set, scaling poorly with its size. We propose a more streamlined approach, prototypical networks, that learns a metric space in which few-shot classification can be performed by computing Euclidean distances to prototype representations of each class, rather than individual points. Our method is competitive with state-of-the-art one-shot classification approaches while being much simpler and more scalable with the size of the support set. We empirically demonstrate the performance of our approach on the Omniglot and mini-ImageNet datasets. We further demonstrate that a similar idea can be used for zero-shot learning, where each class is described by a set of attributes, and achieve state-of-the-art results on the Caltech UCSD bird dataset. Most existing zero-shot learning approaches exploit transfer learning via an intermediate semantic representation shared between an annotated auxiliary dataset and a target dataset with different classes and no annotation. A projection from a low-level feature space to the semantic representation space is learned from the auxiliary dataset and applied without adaptation to the target dataset. In this paper we identify two inherent limitations with these approaches. First, due to having disjoint and potentially unrelated classes, the projection functions learned from the auxiliary dataset domain are biased when applied directly to the target dataset domain. We call this problem the projection domain shift problem and propose a novel framework, transductive multi-view embedding , to solve it. The second limitation is the prototype sparsity problem which refers to the fact that for each target class, only a single prototype is available for zero-shot learning given a semantic representation. To overcome this problem, a novel heterogeneous multi-view hypergraph label propagation method is formulated for zero-shot learning in the transductive embedding space. It effectively exploits the complementary information offered by different semantic representations and takes advantage of the manifold structures of multiple representation spaces in a coherent manner. We demonstrate through extensive experiments that the proposed approach (1) rectifies the projection shift between the auxiliary and target domains, (2) exploits the complementarity of multiple semantic representations, (3) significantly outperforms existing methods for both zero-shot and N-shot recognition on three image and video benchmark datasets, and (4) enables novel cross-view annotation tasks. One-shot learning is a challenging problem where the aim is to recognize a class identified by a single training image. Given the practical importance of one-shot learning, it seems surprising that the rich information present in the class tag itself has largely been ignored. Most existing approaches restrict the use of the class tag to finding similar classes and transferring classifiers or metrics learned thereon. We demonstrate here, in contrast, that the class tag can inform one-shot learning as a guide to visual attention on the training image for creating the image representation. This is motivated by the fact that human beings can better interpret a training image if the class tag of the image is understood. Specifically, we design a neural network architecture which takes the semantic embedding of the class tag to generate attention maps and uses those attention maps to create the image features for one-shot learning. Note that unlike other applications, our task requires that the learned attention generator can be generalized to novel classes. We show that this can be realized by representing class tags with distributed word embeddings and learning the attention map generator from an auxiliary training set. Also, we design a multiple-attention scheme to extract richer information from the exemplar image and this leads to substantial performance improvement. Through comprehensive experiments, we show that the proposed approach leads to superior performance over the baseline methods.