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In order to make financial investment more stable and more profitable, convolutional neural network (CNN) and deep learning technology are used to quantify financial investment, so as to obtain more robust investment and returns. With the continuous development of in-depth learning technology, people are applying it more and more widely. Deep learning is put forward on the basis of neural network. It contains more hidden layers, shows more powerful learning ability, and can abstract data at a higher level, so as to obtain more accurate data. CNN is a multi-layer network structure which simulates the operation mechanism of biological vision system. Its special structure can obtain more useful feature descriptions from original data and is very effective in extracting data. Therefore, in this study, the two technologies are combined to quantify financial investment. The results show that the convolution neural network and deep learning algorithm can obtain relatively accurate investment strategies, thus ensuring investment returns and reducing investment risks. [ABSTRACT FROM AUTHOR]

Pedestrian detection and tracking have become an important field in the computer vision research area. This growing interest, started in the last decades, might be explained by the multitude of potential applications that could use the results of this research field, e.g. robotics, entertainment, surveillance, care for the elderly and disabled, and content-based indexing. In this survey paper, vision-based pedestrian detection systems are analysed based on their field of application, acquisition technology, computer vision techniques and classification strategies. Three main application fields have been individuated and discussed: video surveillance, human-machine interaction and analysis. Due to the large variety of acquisition technologies, this paper discusses both the differences between 2D and 3D vision systems, and indoor and outdoor systems. The authors reserved a dedicated section for the analysis of the Deep Learning methodologies, including the Convolutional Neural Networks in pedestrian detection and tracking, considering their recent exploding adoption for such a kind systems. Finally, focusing on the classification point of view, different Machine Learning techniques have been analysed, basing the discussion on the classification performances on different benchmark datasets. The reported results highlight the importance of testing pedestrian detection systems on different datasets to evaluate the robustness of the computed groups of features used as input to classifiers. [ABSTRACT FROM AUTHOR]

Automatic age and gender classification has been widely used in a large amount of applications, particularly in human-computer interaction, biometrics, visual surveillance, electronic customer, and commercial applications. In this paper, we introduce a hybrid structure which includes Convolutional Neural Network (CNN) and Extreme Learning Machine (ELM), and integrates the synergy of two classifiers to deal with age and gender classification. The hybrid architecture makes the most of their advantages: CNN is used to extract the features from the input images while ELM classifies the intermediate results. We not only give the detailed deployment of our structure including design of parameters and layers, analysis of the hybrid architecture, and the derivation of back-propagation in this system during the iterations, but also adopt several measures to limit the risk of overfitting. After that, two popular datasets, such as, MORPH-II and Adience Benchmark, are used to verify our hybrid structure. Experimental results show that our hybrid architecture outperforms other studies on the same datasets by exhibiting significant performance improvement in terms of accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Different words in discourse arguments usually have varying contributions on the recognition of implicit discourse relations. Following this intuition, we propose two attention-based neural networks, namely inner attention model and outer attention model , to learn better discourse representation by automatically estimating the degrees of relevance of words to discourse relations. The former model only utilizes the information inside discourse arguments, while the latter model builds upon an outside semantic memory to exploit general world knowledge. Both models are capable of assigning more weights to relation-relevant words, and operate in an end-to-end manner. Upon these two models, we further propose a full attention model that combines their strengths into a unified framework. Extensive experiments on the PDTB data set show that our model significantly benefits from highlighting relation-relevant words and yields competitive and even better results against several state-of-the-art systems. [ABSTRACT FROM AUTHOR]

Recently, Convolutional Neural Networks (CNNs) have become a popular choice to tackle image classification tasks. Despite that, it is almost infeasible to embed the CNNs into resource limited hardware (e.g. mobile devices) due to its extremely high memory requirement. To address this problem, several methods were proposed to reduce the CNN memory requirement with a minimum compensation on the classification accuracy. In this paper, we propose a novel one-shot deep compression method based on the fuzzy quantity space to remove redundant CNN weights. Experiments in three public datasets (i.e. MNIST, CIFAR-10 and ImageNet) showed that our proposed approach is able to compress the CNN up to 14 × with a minimal loss of classification accuracy. Also, we present the first attempt to train an end-to-end fuzzy qualitative deep compression model in the fine-art paintings classification problem. We argue that the classification of fine-art collections is a more challenging problem in comparison to objects classification. This is because some of the artworks are neither non-representational nor figurative, and might even require imagination to recognize them. Hence, a question may arise as to whether a machine is able to capture imagination in paintings. One way to find out is by training a deep model and then visualize the low-level to high-level features learnt. Extensive experiments have been conducted on the recently publicly available Wikiart paintings dataset that consists of more than 80,000 paintings and our solution achieve state-of-the-art results (68%) in overall performance. The source code and models are available at: https://github.com/cs-chan/fuzzyDCN . [ABSTRACT FROM AUTHOR]

Scene recognition plays an important role in the task of visual information retrieval, segmentation and image/video understanding. Traditional approaches for scene recognition usually utilize handcrafted features and have the drawbacks of poor representation ability, which can be improved by employing deep convolutional neural network (CNN) features that contain more semantic and structure information and thus possess more discriminative ability via multiple linear and non-linear transformations. However, an amount of detailed information may be lost when only the final output features which have gone through a certain number of transformations are applied to scene recognition. The features which are generated from the intermediate layers are not fully utilized. In this work, the GoogLeNet model is employed and divided into three parts of layers from bottom to top. The output features from each of the three parts are applied for scene recognition, which leads to the proposed GoogLeNet based multi-stage feature fusion (G-MS2F). What's more, the product rule is used to generate the final decision for scene recognition from the three outputs corresponding to the three parts of the proposed model. The experimental results demonstrate that the proposed model is superior to a number of state-of-the-art CNN models for scene recognition, and obtains the recognition accuracy of 92.90%, 79.63% and 64.06% on the benchmark scene recognition datasets Scene15, MIT67 and SUN397, respectively. [ABSTRACT FROM AUTHOR]

In recent years, multimedia event detection has been attracting extensive research attention because of the exponential increase in volume of web video data. Traditional approaches usually utilize single visual representation, which may suffer from the problem of insufficient descriptive power. How to jointly employ multiple types of visual representation to facilitate multimedia event detection (MED) in videos remains an open problem. In this work, we propose a novel system for event detection based on combination of multi-view representations and co-training algorithm. Specifically, given several types of low-level visual features (i.e., Convolutional Neural Networks (CNNs) and Fisher vector), we first train an initial classifier for each type of visual feature. Then, we use these classifiers to separately predict labels of unlabeled videos, and those with consistent prediction are merged into the training set. We alternatively repeat the processes of training the classifiers and enlarging the training set until convergence. To investigate the relationship among different types of visual features, the prediction scores of the two classifiers are fused by a linear weighted fusion method. We evaluate our MED system on the TRECVID MED11 data set, and the experimental results have demonstrated the outstanding performance of the proposed approach as compared to several other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Face representation is a crucial step of face detection system. In this paper, we present a fast face detection algorithm based on representation learnt using convolutional neural network (CNN) so as to explicitly capture various latent facial features. Firstly, in order to improve the speed of detection in the system, we train an Adaboost background filter which can remove the background most quickly. Secondly, we use the CNN to extract more distinctive features for those face and non-face patterns that have not been filtered by Adaboost. CNN can automatically learn and synthesize a problem-specific feature extractor from a training set, without making any assumptions or using any hand-made design concerning the features to extract or the areas of the face pattern to analyze. Finally, support vector machines (SVM) are used to detect instead of using the classification function of CNN itself. Extensive experiments demonstrate the robustness and efficiency of our system by comparing it with several popular face detection algorithms on the widely used CMU+MIT frontal face dataset and FDDB dataset. [ABSTRACT FROM AUTHOR]

This paper proposes an improved stochastic second order learning algorithm for supervised neural network training. The proposed algorithm, named bounded stochastic diagonal Levenberg–Marquardt (B-SDLM), utilizes both gradient and curvature information to achieve fast convergence while requiring only minimal computational overhead than the stochastic gradient descent (SGD) method. B-SDLM has only a single hyperparameter as opposed to most other learning algorithms that suffer from the hyperparameter overfitting problem due to having more hyperparameters to be tuned. Experiments using the multilayer perceptron (MLP) and convolutional neural network (CNN) models have shown that B-SDLM outperforms other learning algorithms with regard to the classification accuracies and computational efficiency (about 5.3% faster than SGD on the mnist-rot-bg-img database). It can classify all testing samples correctly on the face recognition case study based on AR Purdue database. In addition, experiments on handwritten digit classification case studies show that significant improvements of 19.6% on MNIST database and 17.5% on mnist-rot-bg-img database can be achieved in terms of the testing misclassification error rates (MCRs). The computationally expensive Hessian calculations are kept to a minimum by using just 0.05% of the training samples in its estimation or updating the learning rates once per two training epochs, while maintaining or even achieving lower testing MCRs. It is also shown that B-SDLM works well in the mini-batch learning mode, and we are able to achieve 3.32 × performance speedup when deploying the proposed algorithm in a distributed learning environment with a quad-core processor. [ABSTRACT FROM AUTHOR]

Segmentation of retinal blood vessels is of substantial clinical importance for diagnoses of many diseases, such as diabetic retinopathy, hypertension and cardiovascular diseases. In this paper, the supervised method is presented to tackle the problem of retinal blood vessel segmentation, which combines two superior classifiers: Convolutional Neural Network (CNN) and Random Forest (RF). In this method, CNN performs as a trainable hierarchical feature extractor and ensemble RFs work as a trainable classifier. By integrating the merits of feature learning and traditional classifier, the proposed method is able to automatically learn features from the raw images and predict the patterns. Extensive experiments have been conducted on two public retinal images databases (DRIVE and STARE), and comparisons with other major studies on the same database demonstrate the promising performance and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]