pwc-archive/papers-with-abstracts huggingface dataset:
Incremental Classifier Learning Based on PEDCC-Loss and Cosine Distance
Authors: Qiuyu Zhu, Zikuang He, Xin Ye
Date: 2019-06-11 00:00:00
Tasks: Incremental Learning, Knowledge Distillation

The main purpose of incremental learning is to learn new knowledge while not forgetting the knowledge which have been learned before. At present, the main challenge in this area is the catastrophe forgetting, namely the network will lose their performance in the old tasks after training for new tasks. In this paper, we introduce an ensemble method of incremental classifier to alleviate this problem, which is based on the cosine distance between the output feature and the pre-defined center, and can let each task to be preserved in different networks. During training, we make use of PEDCC-Loss to train the CNN network. In the stage of testing, the prediction is determined by the cosine distance between the network latent features and pre-defined center. The experimental results on EMINST and CIFAR100 show that our method outperforms the recent LwF method, which use the knowledge distillation, and iCaRL method, which keep some old samples while training for new task. The method can achieve the goal of not forgetting old knowledge while training new classes, and solve the problem of catastrophic forgetting better.
Different Approaches for Human Activity Recognition: A Survey
Authors: Zawar Hussain, Michael Sheng, Wei Emma Zhang
Date: 2019-06-11 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Survey

Human activity recognition has gained importance in recent years due to its applications in various fields such as health, security and surveillance, entertainment, and intelligent environments. A significant amount of work has been done on human activity recognition and researchers have leveraged different approaches, such as wearable, object-tagged, and device-free, to recognize human activities. In this article, we present a comprehensive survey of the work conducted over the period 2010-2018 in various areas of human activity recognition with main focus on device-free solutions. The device-free approach is becoming very popular due to the fact that the subject is not required to carry anything, instead, the environment is tagged with devices to capture the required information. We propose a new taxonomy for categorizing the research work conducted in the field of activity recognition and divide the existing literature into three sub-areas: action-based, motion-based, and interaction-based. We further divide these areas into ten different sub-topics and present the latest research work in these sub-topics. Unlike previous surveys which focus only on one type of activities, to the best of our knowledge, we cover all the sub-areas in activity recognition and provide a comparison of the latest research work in these sub-areas. Specifically, we discuss the key attributes and design approaches for the work presented. Then we provide extensive analysis based on 10 important metrics, to give the reader, a complete overview of the state-of-the-art techniques and trends in different sub-areas of human activity recognition. In the end, we discuss open research issues and provide future research directions in the field of human activity recognition.
Inferring 3D Shapes from Image Collections using Adversarial Networks
Authors: Matheus Gadelha, Aartika Rai, Subhransu Maji, Rui Wang
Date: 2019-06-11 00:00:00
Tasks: Generative Adversarial Network

We investigate the problem of learning a probabilistic distribution over three-dimensional shapes given two-dimensional views of multiple objects taken from unknown viewpoints. Our approach called projective generative adversarial network (PrGAN) trains a deep generative model of 3D shapes whose projections (or renderings) match the distributions of the provided 2D distribution. The addition of a differentiable projection module allows us to infer the underlying 3D shape distribution without access to any explicit 3D or viewpoint annotation during the learning phase. We show that our approach produces 3D shapes of comparable quality to GANs trained directly on 3D data. %for a number of shape categoriesincluding chairs, airplanes, and cars. Experiments also show that the disentangled representation of 2D shapes into geometry and viewpoint leads to a good generative model of 2D shapes. The key advantage of our model is that it estimates 3D shape, viewpoint, and generates novel views from an input image in a completely unsupervised manner. We further investigate how the generative models can be improved if additional information such as depth, viewpoint or part segmentations is available at training time. To this end, we present new differentiable projection operators that can be used by PrGAN to learn better 3D generative models. Our experiments show that our method can successfully leverage extra visual cues to create more diverse and accurate shapes.
DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections
Authors: Ofir Nachum, Yin-Lam Chow, Bo Dai, Lihong Li
Date: 2019-06-10 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Reinforcement Learning

In many real-world reinforcement learning applications, access to the environment is limited to a fixed dataset, instead of direct (online) interaction with the environment. When using this data for either evaluation or training of a new policy, accurate estimates of discounted stationary distribution ratios -- correction terms which quantify the likelihood that the new policy will experience a certain state-action pair normalized by the probability with which the state-action pair appears in the dataset -- can improve accuracy and performance. In this work, we propose an algorithm, DualDICE, for estimating these quantities. In contrast to previous approaches, our algorithm is agnostic to knowledge of the behavior policy (or policies) used to generate the dataset. Furthermore, it eschews any direct use of importance weights, thus avoiding potential optimization instabilities endemic of previous methods. In addition to providing theoretical guarantees, we present an empirical study of our algorithm applied to off-policy policy evaluation and find that our algorithm significantly improves accuracy compared to existing techniques.
Hierarchical Taxonomy-Aware and Attentional Graph Capsule RCNNs for Large-Scale Multi-Label Text Classification
Authors: Hao Peng, Jian-Xin Li, Qiran Gong, Senzhang Wang, Lifang He, Bo Li, Lihong Wang, Philip S. Yu
Date: 2019-06-09 00:00:00
Tasks: Classification, General Classification, Multi Label Text Classification, Multi-Label Text Classification, Representation Learning, text-classification, Text Classification

CNNs, RNNs, GCNs, and CapsNets have shown significant insights in representation learning and are widely used in various text mining tasks such as large-scale multi-label text classification. However, most existing deep models for multi-label text classification consider either the non-consecutive and long-distance semantics or the sequential semantics, but how to consider them both coherently is less studied. In addition, most existing methods treat output labels as independent methods, but ignore the hierarchical relations among them, leading to useful semantic information loss. In this paper, we propose a novel hierarchical taxonomy-aware and attentional graph capsule recurrent CNNs framework for large-scale multi-label text classification. Specifically, we first propose to model each document as a word order preserved graph-of-words and normalize it as a corresponding words-matrix representation which preserves both the non-consecutive, long-distance and local sequential semantics. Then the words-matrix is input to the proposed attentional graph capsule recurrent CNNs for more effectively learning the semantic features. To leverage the hierarchical relations among the class labels, we propose a hierarchical taxonomy embedding method to learn their representations, and define a novel weighted margin loss by incorporating the label representation similarity. Extensive evaluations on three datasets show that our model significantly improves the performance of large-scale multi-label text classification by comparing with state-of-the-art approaches.
Customizing Pareto Simulated Annealing for Multi-objective Optimization of Control Cabinet Layout
Authors: Sabri Pllana, Suejb Memeti, Joanna Kolodziej
Date: 2019-06-04 00:00:00
Tasks: Position

Determining the optimal location of control cabinet components requires the exploration of a large configuration space. For real-world control cabinets it is impractical to evaluate all possible cabinet configurations. Therefore, we need to apply methods for intelligent exploration of cabinet configuration space that enable to find a near-optimal configuration without evaluation of all possible configurations. In this paper, we describe an approach for multi-objective optimization of control cabinet layout that is based on Pareto Simulated Annealing. Optimization aims at minimizing the total wire length used for interconnection of components and the heat convection within the cabinet. We simulate heat convection to study the warm air flow within the control cabinet and determine the optimal position of components that generate heat during the operation. We evaluate and demonstrate the effectiveness of our approach empirically for various control cabinet sizes and usage scenarios.
eSLAM: An Energy-Efficient Accelerator for Real-Time ORB-SLAM on FPGA Platform
Authors: Runze Liu, Jianlei Yang, Yiran Chen, Weisheng Zhao
Date: 2019-06-03 00:00:00
Tasks: Autonomous Navigation, Simultaneous Localization and Mapping

Simultaneous Localization and Mapping (SLAM) is a critical task for autonomous navigation. However, due to the computational complexity of SLAM algorithms, it is very difficult to achieve real-time implementation on low-power platforms.We propose an energy efficient architecture for real-time ORB (Oriented-FAST and Rotated- BRIEF) based visual SLAM system by accelerating the most time consuming stages of feature extraction and matching on FPGA platform.Moreover, the original ORB descriptor pattern is reformed as a rotational symmetric manner which is much more hardware friendly. Optimizations including rescheduling and parallelizing are further utilized to improve the throughput and reduce the memory footprint. Compared with Intel i7 and ARM Cortex-A9 CPUs on TUM dataset, our FPGA realization achieves up to 3X and 31X frame rate improvement, as well as up to 71X and 25X energy efficiency improvement, respectively.
A Combination of Temporal Sequence Learning and Data Description for Anomaly-based NIDS
Authors: Nguyen Thanh Van, Tran Ngoc Thinh, Le Thanh Sach
Date: 2019-06-07 00:00:00
Tasks: Anomaly Detection, Intrusion Detection, Network Intrusion Detection, Time Series, Time Series Analysis

Through continuous observation and modeling of normal behavior in networks, Anomaly-based Network Intrusion Detection System (A-NIDS) offers a way to find possible threats via deviation from the normal model. The analysis of network traffic based on the time series model has the advantage of exploiting the relationship between packages within network traffic and observing trends of behaviors over a period of time. It will generate new sequences with good features that support anomaly detection in network traffic and provide the ability to detect new attacks. Besides, an anomaly detection technique, which focuses on the normal data and aims to build a description of it, will be an effective technique for anomaly detection in imbalanced data. In this paper, we propose a combination model of Long Short Term Memory (LSTM) architecture for processing time series and a data description Support Vector Data Description (SVDD) for anomaly detection in A-NIDS to obtain the advantages of them. This model helps parameters in LSTM and SVDD are jointly trained with the joint optimization method. Our experimental results with KDD99 dataset show that the proposed combined model obtains high performance in intrusion detection, especially DoS and Probe attacks with 98.0% and 99.8%, respectively.
Co-trained convolutional neural networks for automated detection of prostate cancer in multi-parametric MRI
Authors: Xin Yang, Chaoyue Liu, Zhiwei Wang, Jun Yang, Hung Le Min, Liang Wang, Kwang-Ting (Tim) Cheng
Date: 2017-08-24 00:00:00
Proceeding: Elsevier 2017 8
Tasks: General Classification

Multi-parameter magnetic resonance imaging (mp-MRI) is increasingly popular for prostate cancer (PCa) detection and diagnosis. However, interpreting mp-MRI data which typically contains multiple unregistered 3D sequences, e.g. apparent diffusion coefficient (ADC) and T2-weighted (T2w) images, is time-consuming and demands special expertise, limiting its usage for large-scale PCa screening. Therefore, solutions to computer-aided detection of PCa in mp-MRI images are highly desirable. Most recent advances in automated methods for PCa detection employ a handcrafted feature based two-stage classification flow, i.e. voxel-level classification followed by a region-level classification. This work presents an automated PCa detection system which can concurrently identify the presence of PCa in an image and localize lesions based on deep convolutional neural network (CNN) features and a single-stage SVM classifier. Specifically, the developed co-trained CNNs consist of two parallel convolutional networks for ADC and T2w images respectively. Each network is trained using images of a single modality in a weakly-supervised manner by providing a set of prostate images with image-level labels indicating only the presence of PCa without priors of lesions’ locations. Discriminative visual patterns of lesions can be learned effectively from clutters of prostate and surrounding tissues. A cancer response map with each pixel indicating the likelihood to be cancerous is explicitly generated at the last convolutional layer of the network for each modality. A new back-propagated error E is defined to enforce both optimized classification results and consistent cancer response maps for different modalities, which help capture highly representative PCa-relevant features during the CNN feature learning process. The CNN features of each modality are concatenated and fed into a SVM classifier. For images which are classified to contain cancers, non-maximum suppression and adaptive thresholding are applied to the corresponding cancer response maps for PCa foci localization. Evaluation based on 160 patient data with 12-core systematic TRUS-guided prostate biopsy as the reference standard demonstrates that our system achieves a sensitivity of 0.46, 0.92 and 0.97 at 0.1, 1 and 10 false positives per normal/benign patient which is significantly superior to two state-of-the-art CNN-based methods (Oquab et al., 2015; Zhou et al., 2015) and 6-core systematic prostate biopsies.
Efficient discovery of overlapping communities in massive networks
Authors: Prem K. Gopalan, David M. Blei
Date: 2013-09-03 00:00:00
Proceeding: PNAS 2013 2013 9
Tasks: Articles, Community Detection

Detecting overlapping communities is essential to analyzing and
exploring natural networks such as social networks, biological networks, and citation networks. However, most existing approaches do
not scale to the size of networks that we regularly observe in the real
world. In this paper, we develop a scalable approach to community
detection that discovers overlapping communities in massive realworld networks. Our approach is based on a Bayesian model of networks that allows nodes to participate in multiple communities, and
a corresponding algorithm that naturally interleaves subsampling
from the network and updating an estimate of its communities. We
demonstrate how we can discover the hidden community structure of
several real-world networks, including 3.7 million US patents, 575,000
physics articles from the arXiv preprint server, and 875,000 connected
Web pages from the Internet. Furthermore, we demonstrate on large
simulated networks that our algorithm accurately discovers the true
community structure. This paper opens the door to using sophisticated statistical models to analyze massive networks.
Meta-Optimizing Semantic Evolutionary Search
Authors: Moshe Looks
Date: 2017-07-11 00:00:00
Proceeding: Association for Computing Machinery (ACM) 2017 7
Tasks: Problem Decomposition

I present MOSES (meta-optimizing semantic evolutionary search), a new probabilistic modeling (estimation of distribution) approach to program evolution. Distributions are not estimated over the entire space of programs. Rather, a novel representation-building procedure that exploits domain knowledge is used to dynamically select program subspaces for estimation over. This leads to a system of demes consisting of alternative representations (i.e. program subspaces) that are maintained simultaneously and managed by the overall system. Application of MOSES to solve the artificial ant and hierarchically composed parity-multiplexer problems is described, with results showing superior performance. An analysis of the probabilistic models constructed shows that representation-building allows MOSES to exploit linkages in solving these problems.
Show, Match and Segment: Joint Weakly Supervised Learning of Semantic Matching and Object Co-segmentation
Authors: Yun-Chun Chen, Yen-Yu Lin, Ming-Hsuan Yang, Jia-Bin Huang
Date: 2019-06-13 00:00:00
Tasks: Object, Segmentation, Weakly-supervised Learning

We present an approach for jointly matching and segmenting object instances of the same category within a collection of images. In contrast to existing algorithms that tackle the tasks of semantic matching and object co-segmentation in isolation, our method exploits the complementary nature of the two tasks. The key insights of our method are two-fold. First, the estimated dense correspondence fields from semantic matching provide supervision for object co-segmentation by enforcing consistency between the predicted masks from a pair of images. Second, the predicted object masks from object co-segmentation in turn allow us to reduce the adverse effects due to background clutters for improving semantic matching. Our model is end-to-end trainable and does not require supervision from manually annotated correspondences and object masks. We validate the efficacy of our approach on five benchmark datasets: TSS, Internet, PF-PASCAL, PF-WILLOW, and SPair-71k, and show that our algorithm performs favorably against the state-of-the-art methods on both semantic matching and object co-segmentation tasks.
Detecting Photoshopped Faces by Scripting Photoshop
Authors: Sheng-Yu Wang, Oliver Wang, Andrew Owens, Richard Zhang, Alexei A. Efros
Date: 2019-06-13 00:00:00
Proceeding: ICCV 2019 10
Tasks: Image Manipulation Detection

Most malicious photo manipulations are created using standard image editing tools, such as Adobe Photoshop. We present a method for detecting one very popular Photoshop manipulation -- image warping applied to human faces -- using a model trained entirely using fake images that were automatically generated by scripting Photoshop itself. We show that our model outperforms humans at the task of recognizing manipulated images, can predict the specific location of edits, and in some cases can be used to "undo" a manipulation to reconstruct the original, unedited image. We demonstrate that the system can be successfully applied to real, artist-created image manipulations.
Reweighted Expectation Maximization
Authors: Adji B. Dieng, John Paisley
Date: 2019-06-13 00:00:00
Tasks: Bayesian Inference, Density Estimation, Variational Inference

Training deep generative models with maximum likelihood remains a challenge. The typical workaround is to use variational inference (VI) and maximize a lower bound to the log marginal likelihood of the data. Variational auto-encoders (VAEs) adopt this approach. They further amortize the cost of inference by using a recognition network to parameterize the variational family. Amortized VI scales approximate posterior inference in deep generative models to large datasets. However it introduces an amortization gap and leads to approximate posteriors of reduced expressivity due to the problem known as posterior collapse. In this paper, we consider expectation maximization (EM) as a paradigm for fitting deep generative models. Unlike VI, EM directly maximizes the log marginal likelihood of the data. We rediscover the importance weighted auto-encoder (IWAE) as an instance of EM and propose a new EM-based algorithm for fitting deep generative models called reweighted expectation maximization (REM). REM learns better generative models than the IWAE by decoupling the learning dynamics of the generative model and the recognition network using a separate expressive proposal found by moment matching. We compared REM to the VAE and the IWAE on several density estimation benchmarks and found it leads to significantly better performance as measured by log-likelihood.
Contrastive Multiview Coding
Authors: Yonglong Tian, Dilip Krishnan, Phillip Isola
Date: 2019-06-13 00:00:00
Proceeding: ECCV 2020 8
Tasks: Contrastive Learning, Self-Supervised Action Recognition, Self-Supervised Image Classification

Humans view the world through many sensory channels, e.g., the long-wavelength light channel, viewed by the left eye, or the high-frequency vibrations channel, heard by the right ear. Each view is noisy and incomplete, but important factors, such as physics, geometry, and semantics, tend to be shared between all views (e.g., a "dog" can be seen, heard, and felt). We investigate the classic hypothesis that a powerful representation is one that models view-invariant factors. We study this hypothesis under the framework of multiview contrastive learning, where we learn a representation that aims to maximize mutual information between different views of the same scene but is otherwise compact. Our approach scales to any number of views, and is view-agnostic. We analyze key properties of the approach that make it work, finding that the contrastive loss outperforms a popular alternative based on cross-view prediction, and that the more views we learn from, the better the resulting representation captures underlying scene semantics. Our approach achieves state-of-the-art results on image and video unsupervised learning benchmarks. Code is released at: http://github.com/HobbitLong/CMC/.
Mask2Lesion: Mask-Constrained Adversarial Skin Lesion Image Synthesis
Authors: Kumar Abhishek, Ghassan Hamarneh
Date: 2019-06-13 00:00:00
Tasks: Data Augmentation, Image Generation, Image-to-Image Translation, Lesion Segmentation, Segmentation, Skin Lesion Segmentation

Skin lesion segmentation is a vital task in skin cancer diagnosis and further treatment. Although deep learning based approaches have significantly improved the segmentation accuracy, these algorithms are still reliant on having a large enough dataset in order to achieve adequate results. Inspired by the immense success of generative adversarial networks (GANs), we propose a GAN-based augmentation of the original dataset in order to improve the segmentation performance. In particular, we use the segmentation masks available in the training dataset to train the Mask2Lesion model, and use the model to generate new lesion images given any arbitrary mask, which are then used to augment the original training dataset. We test Mask2Lesion augmentation on the ISBI ISIC 2017 Skin Lesion Segmentation Challenge dataset and achieve an improvement of 5.17% in the mean Dice score as compared to a model trained with only classical data augmentation techniques.