Multi-View Face Recognition From Single RGBD Models of the Faces

This work takes important steps towards solving the following problem of current interest: Assuming that each individual in a population can be modeled by a single frontal RGBD face image, is it possible to carry out face recognition for such a population using multiple 2D images captured from arbitrary viewpoints? Although the general problem as stated above is extremely challenging, it encompasses subproblems that can be addressed today. The subproblems addressed in this work relate to: (1) Generating a large set of viewpoint dependent face images from a single RGBD frontal image for each individual; (2) using hierarchical approaches based on view-partitioned subspaces to represent the training data; and (3) based on these hierarchical approaches, using a weighted voting algorithm to integrate the evidence collected from multiple images of the same face as recorded from different viewpoints. We evaluate our methods on three datasets: a dataset of 10 people that we created and two publicly available datasets which include a total of 48 people. In addition to providing important insights into the nature of this problem, our results show that we are able to successfully recognize faces with accuracies of 95% or higher, outperforming existing state-of-the-art face recognition approaches based on deep convolutional neural networks

Knowledge about the presence or absence of miRNA isoforms (isomiRs) can successfully discriminate amongst 32 TCGA cancer types.

Isoforms of human miRNAs (isomiRs) are constitutively expressed with tissue- and disease-subtype-dependencies. We studied 10 271 tumor datasets from The Cancer Genome Atlas (TCGA) to evaluate whether isomiRs can distinguish amongst 32 TCGA cancers. Unlike previous approaches, we built a classifier that relied solely on \u27binarized\u27 isomiR profiles: each isomiR is simply labeled as \u27present\u27 or \u27absent\u27. The resulting classifier successfully labeled tumor datasets with an average sensitivity of 90% and a false discovery rate (FDR) of 3%, surpassing the performance of expression-based classification. The classifier maintained its power even after a 15× reduction in the number of isomiRs that were used for training. Notably, the classifier could correctly predict the cancer type in non-TCGA datasets from diverse platforms. Our analysis revealed that the most discriminatory isomiRs happen to also be differentially expressed between normal tissue and cancer. Even so, we find that these highly discriminating isomiRs have not been attracting the most research attention in the literature. Given their ability to successfully classify datasets from 32 cancers, isomiRs and our resulting \u27Pan-cancer Atlas\u27 of isomiR expression could serve as a suitable framework to explore novel cancer biomarkers

Task-specific Word Identification from Short Texts Using a Convolutional Neural Network

Task-specific word identification aims to choose the task-related words that best describe a short text. Existing approaches require well-defined seed words or lexical dictionaries (e.g., WordNet), which are often unavailable for many applications such as social discrimination detection and fake review detection. However, we often have a set of labeled short texts where each short text has a task-related class label, e.g., discriminatory or non-discriminatory, specified by users or learned by classification algorithms. In this paper, we focus on identifying task-specific words and phrases from short texts by exploiting their class labels rather than using seed words or lexical dictionaries. We consider the task-specific word and phrase identification as feature learning. We train a convolutional neural network over a set of labeled texts and use score vectors to localize the task-specific words and phrases. Experimental results on sentiment word identification show that our approach significantly outperforms existing methods. We further conduct two case studies to show the effectiveness of our approach. One case study on a crawled tweets dataset demonstrates that our approach can successfully capture the discrimination-related words/phrases. The other case study on fake review detection shows that our approach can identify the fake-review words/phrases.Comment: accepted by Intelligent Data Analysis, an International Journa

On Interpretability of Deep Learning based Skin Lesion Classifiers using Concept Activation Vectors

Deep learning based medical image classifiers have shown remarkable prowess in various application areas like ophthalmology, dermatology, pathology, and radiology. However, the acceptance of these Computer-Aided Diagnosis (CAD) systems in real clinical setups is severely limited primarily because their decision-making process remains largely obscure. This work aims at elucidating a deep learning based medical image classifier by verifying that the model learns and utilizes similar disease-related concepts as described and employed by dermatologists. We used a well-trained and high performing neural network developed by REasoning for COmplex Data (RECOD) Lab for classification of three skin tumours, i.e. Melanocytic Naevi, Melanoma and Seborrheic Keratosis and performed a detailed analysis on its latent space. Two well established and publicly available skin disease datasets, PH2 and derm7pt, are used for experimentation. Human understandable concepts are mapped to RECOD image classification model with the help of Concept Activation Vectors (CAVs), introducing a novel training and significance testing paradigm for CAVs. Our results on an independent evaluation set clearly shows that the classifier learns and encodes human understandable concepts in its latent representation. Additionally, TCAV scores (Testing with CAVs) suggest that the neural network indeed makes use of disease-related concepts in the correct way when making predictions. We anticipate that this work can not only increase confidence of medical practitioners on CAD but also serve as a stepping stone for further development of CAV-based neural network interpretation methods.Comment: Accepted for the IEEE International Joint Conference on Neural Networks (IJCNN) 202

Deep Clustering and Deep Network Compression

The use of deep learning has grown increasingly in recent years, thereby becoming a much-discussed topic across a diverse range of fields, especially in computer vision, text mining, and speech recognition. Deep learning methods have proven to be robust in representation learning and attained extraordinary achievement. Their success is primarily due to the ability of deep learning to discover and automatically learn feature representations by mapping input data into abstract and composite representations in a latent space. Deep learning’s ability to deal with high-level representations from data has inspired us to make use of learned representations, aiming to enhance unsupervised clustering and evaluate the characteristic strength of internal representations to compress and accelerate deep neural networks.Traditional clustering algorithms attain a limited performance as the dimensionality in-creases. Therefore, the ability to extract high-level representations provides beneficial components that can support such clustering algorithms. In this work, we first present DeepCluster, a clustering approach embedded in a deep convolutional auto-encoder. We introduce two clustering methods, namely DCAE-Kmeans and DCAE-GMM. The DeepCluster allows for data points to be grouped into their identical cluster, in the latent space, in a joint-cost function by simultaneously optimizing the clustering objective and the DCAE objective, producing stable representations, which is appropriate for the clustering process. Both qualitative and quantitative evaluations of proposed methods are reported, showing the efficiency of deep clustering on several public datasets in comparison to the previous state-of-the-art methods.Following this, we propose a new version of the DeepCluster model to include varying degrees of discriminative power. This introduces a mechanism which enables the imposition of regularization techniques and the involvement of a supervision component. The key idea of our approach is to distinguish the discriminatory power of numerous structures when searching for a compact structure to form robust clusters. The effectiveness of injecting various levels of discriminatory powers into the learning process is investigated alongside the exploration and analytical study of the discriminatory power obtained through the use of two discriminative attributes: data-driven discriminative attributes with the support of regularization techniques, and supervision discriminative attributes with the support of the supervision component. An evaluation is provided on four different datasets.The use of neural networks in various applications is accompanied by a dramatic increase in computational costs and memory requirements. Making use of the characteristic strength of learned representations, we propose an iterative pruning method that simultaneously identifies the critical neurons and prunes the model during training without involving any pre-training or fine-tuning procedures. We introduce a majority voting technique to compare the activation values among neurons and assign a voting score to evaluate their importance quantitatively. This mechanism effectively reduces model complexity by eliminating the less influential neurons and aims to determine a subset of the whole model that can represent the reference model with much fewer parameters within the training process. Empirically, we demonstrate that our pruning method is robust across various scenarios, including fully-connected networks (FCNs), sparsely-connected networks (SCNs), and Convolutional neural networks (CNNs), using two public datasets.Moreover, we also propose a novel framework to measure the importance of individual hidden units by computing a measure of relevance to identify the most critical filters and prune them to compress and accelerate CNNs. Unlike existing methods, we introduce the use of the activation of feature maps to detect valuable information and the essential semantic parts, with the aim of evaluating the importance of feature maps, inspired by novel neural network interpretability. A majority voting technique based on the degree of alignment between a se-mantic concept and individual hidden unit representations is utilized to evaluate feature maps’ importance quantitatively. We also propose a simple yet effective method to estimate new convolution kernels based on the remaining crucial channels to accomplish effective CNN compression. Experimental results show the effectiveness of our filter selection criteria, which outperforms the state-of-the-art baselines.To conclude, we present a comprehensive, detailed review of time-series data analysis, with emphasis on deep time-series clustering (DTSC), and a founding contribution to the area of applying deep clustering to time-series data by presenting the first case study in the context of movement behavior clustering utilizing the DeepCluster method. The results are promising, showing that the latent space encodes sufficient patterns to facilitate accurate clustering of movement behaviors. Finally, we identify state-of-the-art and present an outlook on this important field of DTSC from five important perspectives