Few-shot Deep Representation Learning based on Information Bottleneck Principle

In a standard anomaly detection problem, a detection model is trained in an unsupervised setting, under an assumption that the samples were generated from a single source of normal data. In practice, however, normal data often consist of multiple classes. In such settings, learning to differentiate between normal instances and anomalies among discrepancies between normal classes without large-scale labeled data presents a significant challenge. In this work, we attempt to overcome this challenge by preparing few examples from each normal class, which is not excessively costly. The above setting can also be described as a few-shot learning for multiple, normal classes, with the goal of learning a useful representation for anomaly detection. In order to utilize the limited labeled examples in training, we integrate the inter-class distances among the labeled examples in the deep feature space into the MAP loss. We derive their relations from an information-theoretic principle. Our empirical study shows that the proposed model improves the segmentation of normal classes in the deep feature space which contributes to identifying the anomaly class examples.

Weak-supervision for Deep Representation Learning under Class Imbalance

Class imbalance is a pervasive issue among classification models including deep learning, whose capacity to extract task-specific features is affected in imbalanced settings. However, the challenges of handling imbalance among a large number of classes, commonly addressed by deep learning, have not received a significant amount of attention in previous studies. In this paper, we propose an extension of the deep over-sampling framework, to exploit automatically-generated abstract-labels, i.e., a type of side-information used in weak-label learning, to enhance deep representation learning against class imbalance. We attempt to exploit the labels to guide the deep representation of instances towards different subspaces, to induce a soft-separation of inherent subtasks of the classification problem. Our empirical study shows that the proposed framework achieves a substantial improvement on image classification benchmarks with imbalanced among large and small numbers of classes.

Discriminative Learning of the Prototype Set for Nearest Neighbor Classification

The nearest neighbor rule is a classic yet essential classification model, particularly in problems where the supervising information is given by pairwise dissimilarities and the embedding function are not easily obtained. Prototype selection provides means of generalization and improving efficiency of the nearest neighbor model, but many existing methods assume and rely on the analyses of the input vector space. In this paper, we explore a dissimilarity-based, parametrized model of the nearest neighbor rule. In the proposed model, the selection of the nearest prototypes is influenced by the parameters of the respective prototypes. It provides a formulation for minimizing the violation of the extended nearest neighbor rule over the training set in a tractable form to exploit numerical techniques. We show that the minimization problem reduces to a large-margin principle learning and demonstrate its advantage by empirical comparisons with other prototype selection methods.

Deep Over-sampling Framework for Classifying Imbalanced Data

Class imbalance is a challenging issue in practical classification problems for deep learning models as well as traditional models. Traditionally successful countermeasures such as synthetic over-sampling have had limited success with complex, structured data handled by deep learning models. In this paper, we propose Deep Over-sampling (DOS), a framework for extending the synthetic over-sampling method to exploit the deep feature space acquired by a convolutional neural network (CNN). Its key feature is an explicit, supervised representation learning, for which the training data presents each raw input sample with a synthetic embedding target in the deep feature space, which is sampled from the linear subspace of in-class neighbors. We implement an iterative process of training the CNN and updating the targets, which induces smaller in-class variance among the embeddings, to increase the discriminative power of the deep representation. We present an empirical study using public benchmarks, which shows that the DOS framework not only counteracts class imbalance better than the existing method, but also improves the performance of the CNN in the standard, balanced settings.