58,545 research outputs found
Match and Reweight Strategy for Generalized Target Shift
We address the problem of unsupervised domain adaptation under the setting of generalized target shift (both class-conditional and label shifts occur). We show that in that setting, for good generalization, it is necessary to learn with similar source and target label distributions and to match the class-conditional probabilities. For this purpose, we propose an estimation of target label proportion by blending mixture estimation and optimal transport. This estimation comes with theoretical guarantees of correctness. Based on the estimation, we learn a model by minimizing a importance weighted loss and a Wasserstein distance between weighted marginals. We prove that this minimization allows to match class-conditionals given mild assumptions on their geometry. Our experimental results show that our method performs better on average than competitors accross a range domain adaptation problems including digits,VisDA and Office
A Multiple Cascade-Classifier System for a Robust and Partially Unsupervised Updating of Land-Cover Maps
A system for a regular updating of land-cover maps is proposed that is based on the use of multitemporal remote-sensing images. Such a system is able to face the updating problem under the realistic but critical constraint that, for the image to be classified (i.e., the most recent of the considered multitemporal data set), no ground truth information is available. The system is composed of an ensemble of partially unsupervised classifiers integrated in a multiple classifier architecture. Each classifier of the ensemble exhibits the following novel peculiarities: i) it is developed in the framework of the cascade-classification approach to exploit the temporal correlation existing between images acquired at different times in the considered area; ii) it is based on a partially unsupervised methodology capable to accomplish the classification process under the aforementioned critical constraint. Both a parametric maximum-likelihood classification approach and a non-parametric radial basis function (RBF) neural-network classification approach are used as basic methods for the development of partially unsupervised cascade classifiers. In addition, in order to generate an effective ensemble of classification algorithms, hybrid maximum-likelihood and RBF neural network cascade classifiers are defined by exploiting the peculiarities of the cascade-classification methodology. The results yielded by the different classifiers are combined by using standard unsupervised combination strategies. This allows the definition of a robust and accurate partially unsupervised classification system capable of analyzing a wide typology of remote-sensing data (e.g., images acquired by passive sensors, SAR images, multisensor and multisource data). Experimental results obtained on a real multitemporal and multisource data set confirm the effectiveness of the proposed system
A self-organising mixture network for density modelling
A completely unsupervised mixture distribution network, namely the self-organising mixture network, is proposed for learning arbitrary density functions. The algorithm minimises the Kullback-Leibler information by means of stochastic approximation methods. The density functions are modelled as mixtures of parametric distributions such as Gaussian and Cauchy. The first layer of the network is similar to the Kohonen's self-organising map (SOM), but with the parameters of the class conditional densities as the learning weights. The winning mechanism is based on maximum posterior probability, and the updating of weights can be limited to a small neighbourhood around the winner. The second layer accumulates the responses of these local nodes, weighted by the learning mixing parameters. The network possesses simple structure and computation, yet yields fast and robust convergence. Experimental results are also presente
A partially unsupervised cascade classifier for the analysis of multitemporal remote-sensing images
A partially unsupervised approach to the classification of multitemporal remote-sensing images is presented. Such an approach allows the automatic classification of a remote-sensing image for which training data are not available, drawing on the information derived from an image acquired in the same area at a previous time. In particular, the proposed technique is based on a cascade classifier approach and on a specific formulation of the expectation-maximization (EM) algorithm used for the unsupervised estimation of the statistical parameters of the image to be classified. The results of experiments carried out on a multitemporal data set confirm the validity of the proposed approach
Labeling the Features Not the Samples: Efficient Video Classification with Minimal Supervision
Feature selection is essential for effective visual recognition. We propose
an efficient joint classifier learning and feature selection method that
discovers sparse, compact representations of input features from a vast sea of
candidates, with an almost unsupervised formulation. Our method requires only
the following knowledge, which we call the \emph{feature sign}---whether or not
a particular feature has on average stronger values over positive samples than
over negatives. We show how this can be estimated using as few as a single
labeled training sample per class. Then, using these feature signs, we extend
an initial supervised learning problem into an (almost) unsupervised clustering
formulation that can incorporate new data without requiring ground truth
labels. Our method works both as a feature selection mechanism and as a fully
competitive classifier. It has important properties, low computational cost and
excellent accuracy, especially in difficult cases of very limited training
data. We experiment on large-scale recognition in video and show superior speed
and performance to established feature selection approaches such as AdaBoost,
Lasso, greedy forward-backward selection, and powerful classifiers such as SVM.Comment: arXiv admin note: text overlap with arXiv:1411.771
Deep Unsupervised Similarity Learning using Partially Ordered Sets
Unsupervised learning of visual similarities is of paramount importance to
computer vision, particularly due to lacking training data for fine-grained
similarities. Deep learning of similarities is often based on relationships
between pairs or triplets of samples. Many of these relations are unreliable
and mutually contradicting, implying inconsistencies when trained without
supervision information that relates different tuples or triplets to each
other. To overcome this problem, we use local estimates of reliable
(dis-)similarities to initially group samples into compact surrogate classes
and use local partial orders of samples to classes to link classes to each
other. Similarity learning is then formulated as a partial ordering task with
soft correspondences of all samples to classes. Adopting a strategy of
self-supervision, a CNN is trained to optimally represent samples in a mutually
consistent manner while updating the classes. The similarity learning and
grouping procedure are integrated in a single model and optimized jointly. The
proposed unsupervised approach shows competitive performance on detailed pose
estimation and object classification.Comment: Accepted for publication at IEEE Computer Vision and Pattern
Recognition 201
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