Recognising relevant objects or object states in its environment is a basic capability for an autonomous robot. The dominant approach to object recognition in images and range images is classification by supervised machine learning, nowadays mostly with deep convolutional neural networks (CNNs). This works well for target classes whose variability can be completely covered with training examples. However, a robot moving in the wild, i.e., in an environment that is not known at the time the recognition system is trained, will often face : the training data cannot be assumed to exhaustively cover all the within-class variability that will be encountered in the test data. In that situation, learning is in principle possible, since the training set does capture the defining properties, respectively dissimilarities, of the target classes. But directly training a CNN to predict class probabilities is prone to overfitting to irrelevant correlations between the class labels and the specific subset of the target class that is represented in the training set. We explore the idea to instead learn a Siamese CNN that acts as similarity function between pairs of training examples. Class predictions are then obtained by measuring the similarities between a new test instance and the training samples. We show that the CNN embedding correctly recovers the relative similarities to arbitrary class exemplars in the training set. And that therefore few, randomly picked training exemplars are sufficient to achieve good predictions, making the procedure efficient.
In their comprehensive survey @cite_13 , classify distance learning methods into three groups, unsupervised distance learning, supervised global distance learning, and supervised local distance learning. The idea of unsupervised methods is to learn a lower-dimensional embedding that preserves the pairwise distances between data points. Besides classical techniques like PCA and its non-linear generalisations (KPCA @cite_8 , LLE @cite_9 , etc.), unsupervised distance learning can also be implemented with neural networks, e.g., auto-encoders @cite_10 .
@cite_10 @cite_9 @cite_13 @cite_8
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In this paper, we propose a novel unsupervised distance metric learning algorithm. The proposed algorithm aims to maximize a stochastic variant of the leave-one-out K-nearest neighbor (KNN) score on unlabeled data, which performs distance metric learning and clustering simultaneously. We show that the joint distance metric learning and clustering problem is formulated as a trace optimization problem, and can be solved efficiently by an iterative algorithm. Moreover, the proposed approach can also learn a low dimensional projection of high dimensional data, thus it can serve as an unsupervised dimensionality reduction tool, which is capable of performing joint dimensionality reduction and clustering. We validate our method on a number of benchmark datasets, and the results demonstrate the effectiveness of the proposed algorithm. Many of the existing methods for learning joint embedding of images and text use only supervised information from paired images and its textual attributes. Taking advantage of the recent success of unsupervised learning in deep neural networks, we propose an end-to-end learning framework that is able to extract more robust multi-modal representations across domains. The proposed method combines representation learning models (i.e., auto-encoders) together with cross-domain learning criteria (i.e., Maximum Mean Discrepancy loss) to learn joint embeddings for semantic and visual features. A novel technique of unsupervised-data adaptation inference is introduced to construct more comprehensive embeddings for both labeled and unlabeled data. We evaluate our method on Animals with Attributes and Caltech-UCSD Birds 200-2011 dataset with a wide range of applications, including zero and few-shot image recognition and retrieval, from inductive to transductive settings. Empirically, we show that our frame-work improves over the current state of the art on many of the considered tasks. With the development of deep learning, supervised learning has frequently been adopted to classify remotely sensed images using convolutional networks. However, due to the limited amount of labeled data available, supervised learning is often difficult to carry out. Therefore, we proposed an unsupervised model called multiple-layer feature-matching generative adversarial networks (MARTA GANs) to learn a representation using only unlabeled data. MARTA GANs consists of both a generative model @math and a discriminative model @math . We treat @math as a feature extractor. To fit the complex properties of remote sensing data, we use a fusion layer to merge the mid-level and global features. @math can produce numerous images that are similar to the training data; therefore, @math can learn better representations of remotely sensed images using the training data provided by @math . The classification results on two widely used remote sensing image databases show that the proposed method significantly improves the classification performance compared with other state-of-the-art methods. This paper investigates an important problem in stream mining, i.e., classification under streaming emerging new classes or SENC . The SENC problem can be decomposed into three subproblems: detecting emerging new classes, classifying known classes, and updating models to integrate each new class as part of known classes. The common approach is to treat it as a classification problem and solve it using either a supervised learner or a semi-supervised learner. We propose an alternative approach by using unsupervised learning as the basis to solve this problem. The proposed method employs completely-random trees which have been shown to work well in unsupervised learning and supervised learning independently in the literature. The completely-random trees are used as a single common core to solve all three subproblems: unsupervised learning, supervised learning, and model update on data streams. We show that the proposed unsupervised-learning-focused method often achieves significantly better outcomes than existing classification-focused methods.