LEARNING FROM INCOMPLETE AND HETEROGENEOUS DATA

Deep convolutional neural networks (DCNNs) have shown impressive performance improvements for object detection and recognition problems. However, a vast majority of DCNN-based recognition methods are designed with two key assumptions in mind, i.e., 1) the assumption that all categories are known a priori and 2) both training and test data are drawn from a similar distribution. However, in many real-world applications, these assumptions do not necessarily hold and limit the generalization capability of a recognition model. Generally, incomplete knowledge of the world is present at training time, and unknown classes can be submitted to an algorithm during testing. If the visual system is trained assuming that all categories are known a priori, it would fail to identify these cases with unknown classes during testing. Ideally, the goal of a visual recognition system would be to reject samples from unknown classes and classify samples from known classes. In this thesis, we consider this constraint and evaluate visual recognition systems under two problem settings, i.e., one-class and multi-class novelty detection. In the one-class setting, the goal is to learn a visual recognition system from a single category and reject any other category samples as unknown during testing. Whereas, in multi-class classification the visual recognition system aims to learn from multiple-categories and reject any other category sample that is not part of the training category set as unknown. With experiments on multiple benchmark datasets we show that the proposed recognition systems are able to perform better compared to existing approaches. Furthermore, we also recognize that in many real world conditions training and testing data distributions are often different. Due to this, the performance of a visual recognition system drops significantly. This is commonly referred to as dataset bias or domain-shift which can be addressed using domain adaptation. In particular, we address unsupervised domain adaptation in which the idea is to utilize an additional set of unlabeled data sampled from a particular domain to help improve the performance in that respective domain. Various experiments on multiple domain adaptation benchmarks show that the proposed strategy is able to generalize better compared to existing methods in the literature