Multi-modal curriculum learning for semi-supervised image classification

Semi-supervised image classification aims to classify a large quantity of unlabeled images by typically harnessing scarce labeled images. Existing semi-supervised methods often suffer from inadequate classification accuracy when encountering difficult yet critical images, such as outliers, because they treat all unlabeled images equally and conduct classifications in an imperfectly ordered sequence. In this paper, we employ the curriculum learning methodology by investigating the difficulty of classifying every unlabeled image. The reliability and the discriminability of these unlabeled images are particularly investigated for evaluating their difficulty. As a result, an optimized image sequence is generated during the iterative propagations, and the unlabeled images are logically classified from simple to difficult. Furthermore, since images are usually characterized by multiple visual feature descriptors, we associate each kind of features with a teacher, and design a multi-modal curriculum learning (MMCL) strategy to integrate the information from different feature modalities. In each propagation, each teacher analyzes the difficulties of the currently unlabeled images from its own modality viewpoint. A consensus is subsequently reached among all the teachers, determining the currently simplest images (i.e., a curriculum), which are to be reliably classified by the multi-modal learner. This well-organized propagation process leveraging multiple teachers and one learner enables our MMCL to outperform five state-of-the-art methods on eight popular image data sets

SeGMA: Semi-Supervised Gaussian Mixture Auto-Encoder

We propose a semi-supervised generative model, SeGMA, which learns a joint probability distribution of data and their classes and which is implemented in a typical Wasserstein auto-encoder framework. We choose a mixture of Gaussians as a target distribution in latent space, which provides a natural splitting of data into clusters. To connect Gaussian components with correct classes, we use a small amount of labeled data and a Gaussian classifier induced by the target distribution. SeGMA is optimized efficiently due to the use of Cramer-Wold distance as a maximum mean discrepancy penalty, which yields a closed-form expression for a mixture of spherical Gaussian components and thus obviates the need of sampling. While SeGMA preserves all properties of its semi-supervised predecessors and achieves at least as good generative performance on standard benchmark data sets, it presents additional features: (a) interpolation between any pair of points in the latent space produces realistically-looking samples; (b) combining the interpolation property with disentangled class and style variables, SeGMA is able to perform a continuous style transfer from one class to another; (c) it is possible to change the intensity of class characteristics in a data point by moving the latent representation of the data point away from specific Gaussian components

Safe Semi-Supervised Learning with Sparse Graphs

There has been substantial interest from both computer science and statistics in developing methods for graph-based semi-supervised learning. The attraction to the area involves several challenging applications brought forth from academia and industry where little data are available with training responses while lots of data are available overall. Ample evidence has demonstrated the value of several of these methods on real data applications, but it should be kept in mind that they heavily rely on some smoothness assumptions. The general frame- work for graph-based semi-supervised learning is to optimize a smooth function over the nodes of the proximity graph constructed from the feature data which is extremely time consuming as the conventional methods for graph construction in general create a dense graph. Lately the interest has shifted to developing faster and more efficient graph-based techniques on larger data, but it comes with a cost of reduced prediction accuracies and small areas of application. The focus of this research is to generate a graph-based semi-supervised model that attains fast convergence without losing its performance and with a larger applicability. The key feature of the semi-supervised model is that it does not fully rely on the smoothness assumptions and performs adequately on real data. Another model is proposed for the case with availability of multiple views. Empirical analysis with real and simulated data showed the competitive performance of the methods against other machine learning algorithms

IEEE Transactions On Neural Networks And Learning Systems : Vol. 24, No. 11, November 2013

1. Error surface of recurrent neural networks. 2. Single-channel blind separation using pseudo-stereo mixture and complex 2-d histogram. 3. On the SVMpath singularity. 4. Multistability of two kinds of recurrent neural networks with activation functions symmetrical about the origin on the phase plane. 5. Safety-aware semi-supervised classification. 6. Neural network approaches for noisy language modeling. 7. A New discrete-continuous algorithm for radial basis function networks construction. 8. Finding potential support vectors in separable classification problems. 9. Nonlinear systems identification and control via dynamic multitime scale neural networks. 10. Hierarchical similarity transformations between gaussian mixtures. 11. Negative correlation ensemble learning for ordinal regression. 12. Online learning of a dirichlet process mixture of beta-liouville distributions via variational inference. 13. Transfer ordinal label learning. 14. Pseudo-orthogonalization of memory patterns for associative memory. 15. Multilabel classification using error-correcting codes of hard or soft bits. 16. Multiclass support vector machines with examples-dependent costs applied to plankton biomass estimation. 17. Correction to: "Estimator design for discrete-time switched neural networks with asynchronous switching and time-varying delay" Etc