Representation Learning: A Review and New Perspectives

The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, auto-encoders, manifold learning, and deep networks. This motivates longer-term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation and manifold learning

Deep Learning For Sequential Pattern Recognition

Projecte realitzat en el marc d’un programa de mobilitat amb la Technische Universität München (TUM)In recent years, deep learning has opened a new research line in pattern recognition tasks. It has been hypothesized that this kind of learning would capture more abstract patterns concealed in data. It is motivated by the new findings both in biological aspects of the brain and hardware developments which have made the parallel processing possible. Deep learning methods come along with the conventional algorithms for optimization and training make them efficient for variety of applications in signal processing and pattern recognition. This thesis explores these novel techniques and their related algorithms. It addresses and compares different attributes of these methods, sketches in their possible advantages and disadvantages

Efficient object detection via structured learning and local classifiers

Object detection has made great strides recently. However, it is still facing two big challenges: detection accuracy and computational efficiency. In this thesis, we present an automatic efficient object detection frarnework to detect object instances ·in images using bounding boxes, which can be trained and tested easily on current personal computers. Our framework is a sliding-window based approach, and consists of two major components: (1) efficient object proposal generation, predicting possible object bounding boxes, and (2) efficient object proposal verification, classifying each bounding box in a multiclass manner. For object proposal generation, we formulate this problem as a structured learning problem and investigate structural support vector machines (SSVMs) with our proposed scale/aspect-ratio quantization scheme and ranking constraints. A general ranking-order decomposition algorithm is developed for solving the formulation efficiently, and applied to generate proposals using a two-stage cascade. Using image gradients as features, our object proposal generation method achieves state-of-the-art results in terms Df object recall at a low cost in computation. For object proposal verification, we propose two locally linear and one locally nonlinear classifiers to approximate the nonlinear decision boundaries in the feature space efficiently. Inspired by the kernel trick, these classifiers map the original features into another feature space explicitly where linear classifiers are employed for classification, and thus have linear computational complexity in both training and testing, similar to that of linear classifiers. Therefore, in general, our classifiers can achieve comparable accuracy to kernel based classifiers at the cost of lower computational time. To demonstrate its efficiency and generality, our framework is applied to four different object detection tasks: VOC detection challenges, traffic sign detection, pedestrian detection, and face detection. In each task, it can perform reasonably well with acceptable detection accuracy and good computational efficiency. For instance, on VOC datasets with 20 object classes, our method achieved about 0.1 mean average precision (AP) within 2 hours of training and 0.05 second of testing a 500 x 300 pixel image using a mixture of MATLAB and C++ code on a current personal computer

Sparse Coding with Structured Sparsity Priors and Multilayer Architecture for Image Classification

Applying sparse coding on large dataset for image classification is a long standing problem in the field of computer vision. It has been found that the sparse coding models exhibit disappointing performance on these large datasets where variability is broad and anomalies are common. Conversely, deep neural networks thrive on bountiful data. Their success has encouraged researchers to try and augment the learning capacity of traditionally shallow sparse coding methods by adding layers. Multilayer sparse coding networks are expected to combine the best of both sparsity regularizations and deep architectures. To date, however, endeavors to marry the two techniques have not achieved significant improvements over their individual counterparts. In this thesis, we first briefly review multiple structured sparsity priors as well as various supervised dictionary learning techniques with applications on hyperspectral image classification. Based on the structured sparsity priors and dictionary learning techniques, we then develop a novel multilayer sparse coding network that contains thirteen sparse coding layers. The proposed sparse coding network learns both the dictionaries and the regularization parameters simultaneously using an end-to-end supervised learning scheme. We show empirical evidence that the regularization parameters can adapt to the given training data. We also propose applying dimension reduction within sparse coding networks to dramatically reduce the output dimensionality of the sparse coding layers and mitigate computational costs. Moreover, our sparse coding network is compatible with other powerful deep learning techniques such as drop out, batch normalization and shortcut connections. Experimental results show that the proposed multilayer sparse coding network produces classification accuracy competitive with the deep neural networks while using significantly fewer parameters and layers