You Look Twice: GaterNet for Dynamic Filter Selection in CNNs

Convolutional Neural Network (CNN) is a powerful and successful deep learning technique for a variety of computer vision and image analysis applications. Interpreting and explaining the decisions of CNNs is one of the most challengeable tasks despite its significant success in various image analysis tasks. Topological Data Analysis (TDA) is an approach that exploits algebraic invariants from topology to analyse high dimensional and noisy datasets as well as growing challenges of big data applications. Persistent homology (PH) is an algebraic topology method for measuring topological features of shapes and/or functions at different distance or similarity resolutions. This work is an attempt to investigate the algebraic properties of pretrained CNN convolutional layer filters based on random Gaussian/Uniform distribution. We shall investigate the stability and sensitivity of the condition number of CNN filters during and post the model training with focus on class discriminability of the PH features of the convolved images. We shall demonstrate a strong link between the condition number of the CNN filters and their discriminating power of the PH representation. In particular, we shall establish that if small perturbation added to the original images then feature maps with well-conditioned filters will produce similar topological features to the original image. Our investigation and findings are based on training CNN's with Digits, MNIST and CIFAR-10 datasets. Our ultimate interest in applying the results of these findings in designing appropriate CNN models for classifications of ultrasound tumor scan images. Preliminary results for these applications are encouraging.

Convolutional neural network (CNN)-based in-loop filtering have been very successful in video coding. For most existing works, however, a specific model was required for each quantization parameter (QP) band. In this paper, we introduce a generic method for helping CNN-filters deal with variable quantization noises. A feasible solution to this problem can be implemented on CNN by introducing a quantization step (Qstep) into the CNN. As the quantization noise changes, the CNN filter's ability to suppress noise changes accordingly. The (vanilla) convolution layer can be replaced directly by this method in existing CNN filters. Compared with the VVenC anchor, only one CNN filter is used and achieves about 3.6% BD-rate reduction for the luminance component of random-access configuration. Also, about 0.8% BD-rate reduction has been achieved compared with the previous QP-map method.

The report presents an algorithm for constructing a convolutional hierarchical neural network classifier, which is a modification of the algorithm for constructing hierarchical neural network classifiers suggested before. The original algorithm was designed to exploit intrinsic class hierarchy to build a class tree with a neural network in each node classifying groups of initial classes (in a non-terminal node) or a subset of original classes (in a terminal node). The convolutional modification utilizes convolutional neural networks instead of regular fully connected networks in order to apply the model to image classification tasks. Use of class hierarchy for image classification should reduce the number of adjusted neural network parameters compared to deep convolutional neural networks, and therefore it should reduce training and inference time. In this context the algorithm may be compared with some pruning techniques. The convolutional hierarchical neural network classifier inherits some hyperparameters of a conventional hierarchical neural network classifier, like the activation threshold and the threshold by the share of voting patterns. The goal of this study was to explore different strategies of choosing these hyperparameters. To test these strategies, we used the CIFAR-10 dataset. Also, for demonstration purposes we apply the convolutional hierarchical neural network classifier to the CIFAR-100 dataset.

Self-mixing interferometry (SMI) is superior to other laser interferometry methods due to its simplicity and compactness. However, SMI signals are often complex and difficult to process due to interference in the form of variations in the effective reflectivity of the target, noisy signals, complex signal shapes, and other dependencies. Deep neural networks have been a very popular area of research in computer artificial intelligence in recent years, allowing more implicit features to be uncovered than traditional shallow machine learning. It has been shown that convolutional and back-propagation neural networks can be used for SMI signal processing. There are also studies that have used machine learning genetic algorithms for absolute distance measurement. Based on the above, this study used convolutional neural networks to form a deep neural network for absolute distance measurement based on SMI technology. We first trained the deep convolutional neural network at different feedback strengths. The results of the Convolutional Neural Network (CNN) model showed a coefficient of determination of 0.9987. which is consistent with the required model. The trained network was then used to estimate absolute distances with and without the addition of noise. The comparison proves that the proposed method is noise-proof and has high adaptability for measurements under different conditions.

Identification of crops is an important topic in the agricultural domain. Hyperspectral remote sensing data are very useful for crop feature extraction and classification. Remote sensing data is an unstructured data and Convolutional Neural Network (CNN) can work on unstructured data efficiently. This paper presents an evaluation of CNN for crop classification using the Indian Pines standard dataset obtained from the AVIRIS sensor and the study area dataset obtained from the EO-1hyperion sensor. Optimized CNN has been tuned by training the model on different parameters. It has been compared with two classification algorithms: Deep Neural Network (DNN) and Convolutional Autoencoder. According to the test results, the proposed optimized CNN model provided better results as compared to the other two methods. CNN has given 97 ± 0.58% overall accuracy for the Indian Pines standard dataset and 78 ± 2.43% for our study area dataset.

Filter pruning is a technique that reduces computational complexity, inference time, and memory footprint by removing unnecessary filters in convolutional neural networks (CNNs) with an acceptable drop in accuracy, consequently accelerating the network. Unlike traditional filter pruning methods utilizing zeroing-out filters, we propose two techniques to achieve the effect of pruning more filters with less performance degradation, inspired by the existing research on centripetal stochastic gradient descent (C-SGD), wherein the filters are removed only when the ones that need to be pruned have the same value. First, to minimize the negative effect of centripetal vectors that gradually make filters come closer to each other, we redesign the vectors by considering the effect of each vector on the loss-function using the Taylor-based method. Second, we propose an adaptive gradient learning (AGL) technique that updates weights while adaptively changing the gradients. Through AGL, performance degradation can be mitigated because some gradients maintain their original direction, and AGL also minimizes the accuracy loss by perfectly converging the filters, which require pruning, to a single point. Finally, we demonstrate the superiority of the proposed method on various datasets and networks. In particular, on the ILSVRC-2012 dataset, our method removed 52.09% FLOPs with a negligible 0.15% top-1 accuracy drop on ResNet-50. As a result, we achieve the most outstanding performance compared to those reported in previous studies in terms of the trade-off between accuracy and computational complexity.

IIRI-Net: An interpretable convolutional front-end inspired by IIR filters for speaker identification

paper, we focus on exploring the relationship between weave patterns and their mechanical properties in woven fiber composites through Machine Learning. Specifically, we explore the interactions between woven architectures and in-plane stiffness properties through Deep Convolutional Neural Network (DCNN) and Generative Adversarial Network (GAN). Our research is important for exploring how woven composite’s pattern is related to its mechanical properties and accelerating woven composite design as well as optimization. We focus on two tasks: (1) Stiffness prediction: Predicting in-plane stiffness properties for given weave patterns. Our DCNN extracts high-level features through several convolutional and fully connected layers to determine the final predictions. (2) Weave pattern prediction: Predicting weave patterns for target stiffness properties, which can be treated as the reverse task of the first one. Due to many-to-one mapping between weave patterns and the composite properties, we utilize a Decoder Neural Network as our baseline model and compare its performance with GAN and Genetic Algorithm. We represent the weave patterns as 2D checkerboard models and use finite element analysis (FEA) to determine in-plane stiffness properties, which serve as input data for our ML framework. We show that: (1) for stiffness prediction, DCNN can predict stiffness values for a given weave pattern with relatively high accuracy (above 93%); (2) for weave pattern prediction, the GAN model gives the best prediction accuracy (above 92%) while Decoder Neural Network has the best time efficiency. HAOTIAN FENG

Evaluation of the benchmark datasets for testing the efficacy of deep convolutional neural networks

Text recognition in natural scene images is a challenging problem in computer vision. Different than the optical character recognition (OCR), text recognition in natural scene images is more complex due to variations in text size, colors, fonts, orientations, complex backgrounds, occlusion, illuminations and uneven lighting conditions. In this paper, we propose a segmentation-free method based on a deep convolutional recurrent neural network to solve the problem of cursive text recognition, particularly focusing on Urdu text in natural scenes. Compared to the non-cursive scripts, Urdu text recognition is more complex due to variations in the writing styles, several shapes of the same character, connected text, ligature overlapping, stretched, diagonal and condensed text. The proposed model gets a whole word image as an input without pre-segmenting into individual characters, and then transforms into the sequence of the relevant features. Our model is based on three components: a deep convolutional neural network (CNN) with shortcut connections to extract and encode the features, a recurrent neural network (RNN) to decode the convolutional features, and a connectionist temporal classification (CTC) to map the predicted sequences into the target labels. To increase the text recognition accuracy further, we explore deeper CNN architectures like VGG-16, VGG-19, ResNet-18 and ResNet-34 to extract more appropriate Urdu text features, and compare the recognition results. To conduct the experiments, a new large-scale benchmark dataset of cropped Urdu word images in natural scenes is developed. The experimental results show that the proposed deep CRNN network with shortcut connections outperform than other network architectures. The dataset is publicly available and can be downloaded from <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://data.mendeley.com/datasets/k5fz57zd9z/1</uri> .

In the indoor environment, the activity of the pedes-trian can reflect some semantic information, for example, if a user’s activity is recognized as taking elevator, the location of the user is inferred to be in the elevator. These activities can be used as the landmarks for indoor localization. The development of sensor technology enhances the smartphone’s sensing and computational capabilities. Using the smartphones, the activity of the pedestrian can be recognized. Current methods rely on extracting complex hand-crafted features, thus leading to the incapability of real time pedestrian activities identification. In this paper, we propose a real time pedestrian activities recognition method based on deep convolutional neural network. A new deep convolutional neural (CNN) network has been designed to learn the proper features automatically. Experiments show that the proposed method achieves approximately 95.21% accuracy in about 2 seconds in identifying nine types of activities, including still, walk, upstairs, up elevator, up escalator, down elevator, down escalator, downstairs and turning. Besides, we transplant the activity recognition algorithm to smartphones using tensorflow. Moreover, we have built a pedestrian activity database, which contains more than 6 GB data of accelerometer, magnetometer, gyroscope and barometer collected with various types of smartphones. We will make it public to contribute the academic research.

Convolutional neural networks (CNNs) are of great interest and have demonstrated remarkable performance in hyperspectral images (HSIs) classification. However, due to the current configuration of the convolution layers with a fixed kernel shape, regular CNNs are inherently limited in modeling the diverse land-cover structures, particularly in the cross-classes edge regions, where irregular class boundaries would lead to high classification errors. To address this issue, we propose a content-guided CNN (CGCNN) for HSI classification. Compared with the shape-fixed kernel in the traditional CNN, the proposed content-guided convolution adaptively adjusts its kernel shape according to the spatial distribution of land covers. The content pattern is reflected by a latent guide map automatically learned from HSI. Such content-adaptive kernel with CGCNN could suppress the irregularity and unexpected features in class boundaries and, thus, improve the feature learning in cross-classes regions. Based on the content-guided convolution, a novel guided feature extraction unit (GFEU) is constructed for spectral–spatial feature learning of HSI. Finally, the CGCNN classification framework is established by stacking multiple GFEUs with dense connection, which is helpful for mitigating the gradient vanishing and increasing the robustness to overfitting. Extensive experiments on several HSIs demonstrate that the proposed approach possesses great details’ preserving ability and its performance outperforms other state-of-the-art methods.

Suppression of interference fringes in wavelength modulation spectroscopy based on convolutional neural networks

Deep Convolutional Neural Networks for Large-scale Speech Tasks

Deep convolutional neural networks have become a dominant solution for numerous image classification tasks. However, a main criticism is the poor explainability due to the black-box characteristic, which hurdles the extensive usage of deep convolutional neural networks. To address this issue, this paper proposes a new evolutionary multi-objective based method, which aims to explain the behaviours of deep convolutional neural networks by evolving local explanations on specific images. To the best of our knowledge, this is the first evolutionary multi-objective method to evolve local explanations. The proposed method is model-agnostic, i.e. it is applicable to explain any deep convolutional neural networks. ImageNet is used to examine the effectiveness of the proposed method. Three well-known deep convolutional neural networks -VGGNet, ResNet, and MobileNet, are chosen to demonstrate the modelagnostic characteristic. Based on the experimental results, it can be observed that the local explanations are understandable to end-users, who need to check the sensibility of the evolved explanations to decide whether to trust the predictions made by the deep convolutional neural networks. Furthermore, the local explanations evolved by the proposed method improves the confidence of deep convolutional neural networks making the predictions. Lastly, the pareto front and convergence analyses indicate that the proposed method can form a good set of nondominated solutions.