January 28, 2020

3360 words 16 mins read

Paper Group ANR 903

Feature Selection and Feature Extraction in Pattern Analysis: A Literature Review. Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness. ACE: Adapting to Changing Environments for Semantic Segmentation. Synthesizing Visual Illusions Using Generative Adversarial Networks. Deep Multi-class Adversarial Specularity Removal. Ban …

Feature Selection and Feature Extraction in Pattern Analysis: A Literature Review


Title	Feature Selection and Feature Extraction in Pattern Analysis: A Literature Review
Authors	Benyamin Ghojogh, Maria N. Samad, Sayema Asif Mashhadi, Tania Kapoor, Wahab Ali, Fakhri Karray, Mark Crowley
Abstract	Pattern analysis often requires a pre-processing stage for extracting or selecting features in order to help the classification, prediction, or clustering stage discriminate or represent the data in a better way. The reason for this requirement is that the raw data are complex and difficult to process without extracting or selecting appropriate features beforehand. This paper reviews theory and motivation of different common methods of feature selection and extraction and introduces some of their applications. Some numerical implementations are also shown for these methods. Finally, the methods in feature selection and extraction are compared.
Tasks	Feature Selection
Published	2019-05-07
URL	https://arxiv.org/abs/1905.02845v1
PDF	https://arxiv.org/pdf/1905.02845v1.pdf
PWC	https://paperswithcode.com/paper/feature-selection-and-feature-extraction-in
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Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness


Title	Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness
Authors	Jörn-Henrik Jacobsen, Jens Behrmannn, Nicholas Carlini, Florian Tramèr, Nicolas Papernot
Abstract	Adversarial examples are malicious inputs crafted to cause a model to misclassify them. Their most common instantiation, “perturbation-based” adversarial examples introduce changes to the input that leave its true label unchanged, yet result in a different model prediction. Conversely, “invariance-based” adversarial examples insert changes to the input that leave the model’s prediction unaffected despite the underlying input’s label having changed. In this paper, we demonstrate that robustness to perturbation-based adversarial examples is not only insufficient for general robustness, but worse, it can also increase vulnerability of the model to invariance-based adversarial examples. In addition to analytical constructions, we empirically study vision classifiers with state-of-the-art robustness to perturbation-based adversaries constrained by an $\ell_p$ norm. We mount attacks that exploit excessive model invariance in directions relevant to the task, which are able to find adversarial examples within the $\ell_p$ ball. In fact, we find that classifiers trained to be $\ell_p$-norm robust are more vulnerable to invariance-based adversarial examples than their undefended counterparts. Excessive invariance is not limited to models trained to be robust to perturbation-based $\ell_p$-norm adversaries. In fact, we argue that the term adversarial example is used to capture a series of model limitations, some of which may not have been discovered yet. Accordingly, we call for a set of precise definitions that taxonomize and address each of these shortcomings in learning.
Tasks
Published	2019-03-25
URL	http://arxiv.org/abs/1903.10484v1
PDF	http://arxiv.org/pdf/1903.10484v1.pdf
PWC	https://paperswithcode.com/paper/exploiting-excessive-invariance-caused-by
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ACE: Adapting to Changing Environments for Semantic Segmentation


Title	ACE: Adapting to Changing Environments for Semantic Segmentation
Authors	Zuxuan Wu, Xin Wang, Joseph E. Gonzalez, Tom Goldstein, Larry S. Davis
Abstract	Deep neural networks exhibit exceptional accuracy when they are trained and tested on the same data distributions. However, neural classifiers are often extremely brittle when confronted with domain shift—changes in the input distribution that occur over time. We present ACE, a framework for semantic segmentation that dynamically adapts to changing environments over the time. By aligning the distribution of labeled training data from the original source domain with the distribution of incoming data in a shifted domain, ACE synthesizes labeled training data for environments as it sees them. This stylized data is then used to update a segmentation model so that it performs well in new environments. To avoid forgetting knowledge from past environments, we introduce a memory that stores feature statistics from previously seen domains. These statistics can be used to replay images in any of the previously observed domains, thus preventing catastrophic forgetting. In addition to standard batch training using stochastic gradient decent (SGD), we also experiment with fast adaptation methods based on adaptive meta-learning. Extensive experiments are conducted on two datasets from SYNTHIA, the results demonstrate the effectiveness of the proposed approach when adapting to a number of tasks.
Tasks	Meta-Learning, Semantic Segmentation
Published	2019-04-12
URL	http://arxiv.org/abs/1904.06268v1
PDF	http://arxiv.org/pdf/1904.06268v1.pdf
PWC	https://paperswithcode.com/paper/ace-adapting-to-changing-environments-for
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Synthesizing Visual Illusions Using Generative Adversarial Networks


Title	Synthesizing Visual Illusions Using Generative Adversarial Networks
Authors	Alexander Gomez-Villa, Adrian Martín, Javier Vazquez-Corral, Jesús Malo, Marcelo Bertalmío
Abstract	Visual illusions are a very useful tool for vision scientists, because they allow them to better probe the limits, thresholds and errors of the visual system. In this work we introduce the first ever framework to generate novel visual illusions with an artificial neural network (ANN). It takes the form of a generative adversarial network, with a generator of visual illusion candidates and two discriminator modules, one for the inducer background and another that decides whether or not the candidate is indeed an illusion. The generality of the model is exemplified by synthesizing illusions of different types, and validated with psychophysical experiments that corroborate that the outputs of our ANN are indeed visual illusions to human observers. Apart from synthesizing new visual illusions, which may help vision researchers, the proposed model has the potential to open new ways to study the similarities and differences between ANN and human visual perception.
Tasks
Published	2019-11-21
URL	https://arxiv.org/abs/1911.09599v1
PDF	https://arxiv.org/pdf/1911.09599v1.pdf
PWC	https://paperswithcode.com/paper/synthesizing-visual-illusions-using
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Deep Multi-class Adversarial Specularity Removal


Title	Deep Multi-class Adversarial Specularity Removal
Authors	John Lin, Mohamed El Amine Seddik, Mohamed Tamaazousti, Youssef Tamaazousti, Adrien Bartoli
Abstract	We propose a novel learning approach, in the form of a fully-convolutional neural network (CNN), which automatically and consistently removes specular highlights from a single image by generating its diffuse component. To train the generative network, we define an adversarial loss on a discriminative network as in the GAN framework and combined it with a content loss. In contrast to existing GAN approaches, we implemented the discriminator to be a multi-class classifier instead of a binary one, to find more constraining features. This helps the network pinpoint the diffuse manifold by providing two more gradient terms. We also rendered a synthetic dataset designed to help the network generalize well. We show that our model performs well across various synthetic and real images and outperforms the state-of-the-art in consistency.
Tasks
Published	2019-04-04
URL	http://arxiv.org/abs/1904.02672v1
PDF	http://arxiv.org/pdf/1904.02672v1.pdf
PWC	https://paperswithcode.com/paper/deep-multi-class-adversarial-specularity
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Bandit Convex Optimization in Non-stationary Environments


Title	Bandit Convex Optimization in Non-stationary Environments
Authors	Peng Zhao, Guanghui Wang, Lijun Zhang, Zhi-Hua Zhou
Abstract	Bandit Convex Optimization (BCO) is a fundamental framework for modeling sequential decision-making with partial information, where the only feedback available to the player is the one-point or two-point function values. In this paper, we investigate BCO in non-stationary environments and choose the \emph{dynamic regret} as the performance measure, which is defined as the difference between the cumulative loss incurred by the algorithm and that of any feasible comparator sequence. Let $T$ be the time horizon and $P_T$ be the path-length of the comparator sequence that reflects the non-stationarity of environments. We propose a novel algorithm that achieves $O(T^{3/4}(1+P_T)^{1/2})$ and $O(T^{1/2}(1+P_T)^{1/2})$ dynamic regret respectively for the one-point and two-point feedback models. The latter result is optimal, matching the $\Omega(T^{1/2}(1+P_T)^{1/2})$ lower bound established in this paper. Notably, our algorithm is more adaptive to non-stationary environments since it does not require prior knowledge of the path-length $P_T$ ahead of time, which is generally unknown.
Tasks	Decision Making
Published	2019-07-29
URL	https://arxiv.org/abs/1907.12340v2
PDF	https://arxiv.org/pdf/1907.12340v2.pdf
PWC	https://paperswithcode.com/paper/bandit-convex-optimization-in-non-stationary
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A Sufficient Statistic for Influence in Structured Multiagent Environments


Title	A Sufficient Statistic for Influence in Structured Multiagent Environments
Authors	Frans A. Oliehoek, Stefan Witwicki, Leslie P. Kaelbling
Abstract	Making decisions in complex environments is a key challenge in artificial intelligence (AI). Situations involving multiple decision makers are particularly complex, leading to computation intractability of principled solution methods. A body of work in AI [4, 3, 41, 45, 47, 2] has tried to mitigate this problem by trying to bring down interaction to its core: how does the policy of one agent influence another agent? If we can find more compact representations of such influence, this can help us deal with the complexity, for instance by searching the space of influences rather than that of policies [45]. However, so far these notions of influence have been restricted in their applicability to special cases of interaction. In this paper we formalize influence-based abstraction (IBA), which facilitates the elimination of latent state factors without any loss in value, for a very general class of problems described as factored partially observable stochastic games (fPOSGs) [33]. This generalizes existing descriptions of influence, and thus can serve as the foundation for improvements in scalability and other insights in decision making in complex settings.
Tasks	Decision Making
Published	2019-07-22
URL	https://arxiv.org/abs/1907.09278v1
PDF	https://arxiv.org/pdf/1907.09278v1.pdf
PWC	https://paperswithcode.com/paper/a-sufficient-statistic-for-influence-in
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Stacked Semantic-Guided Network for Zero-Shot Sketch-Based Image Retrieval


Title	Stacked Semantic-Guided Network for Zero-Shot Sketch-Based Image Retrieval
Authors	Hao Wang, Cheng Deng, Xinxu Xu, Wei Liu, Xinbo Gao, Dacheng Tao
Abstract	Zero-shot sketch-based image retrieval (ZS-SBIR) is a task of cross-domain image retrieval from a natural image gallery with free-hand sketch under a zero-shot scenario. Previous works mostly focus on a generative approach that takes a highly abstract and sparse sketch as input and then synthesizes the corresponding natural image. However, the intrinsic visual sparsity and large intra-class variance of the sketch make the learning of the conditional decoder more difficult and hence achieve unsatisfactory retrieval performance. In this paper, we propose a novel stacked semantic-guided network to address the unique characteristics of sketches in ZS-SBIR. Specifically, we devise multi-layer feature fusion networks that incorporate different intermediate feature representation information in a deep neural network to alleviate the intrinsic sparsity of sketches. In order to improve visual knowledge transfer from seen to unseen classes, we elaborate a coarse-to-fine conditional decoder that generates coarse-grained category-specific corresponding features first (taking auxiliary semantic information as conditional input) and then generates fine-grained instance-specific corresponding features (taking sketch representation as conditional input). Furthermore, regression loss and classification loss are utilized to preserve the semantic and discriminative information of the synthesized features respectively. Extensive experiments on the large-scale Sketchy dataset and TU-Berlin dataset demonstrate that our proposed approach outperforms state-of-the-art methods by more than 20% in retrieval performance.
Tasks	Image Retrieval, Sketch-Based Image Retrieval, Transfer Learning
Published	2019-04-03
URL	https://arxiv.org/abs/1904.01971v2
PDF	https://arxiv.org/pdf/1904.01971v2.pdf
PWC	https://paperswithcode.com/paper/stacked-semantic-guided-network-for-zero-shot
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Extended Local Binary Patterns for Efficient and Robust Spontaneous Facial Micro-Expression Recognition


Title	Extended Local Binary Patterns for Efficient and Robust Spontaneous Facial Micro-Expression Recognition
Authors	Chengyu Guo, Jingyun Liang, Geng Zhan, Zhong Liu, Matti Pietikäinen, Li Liu
Abstract	Facial Micro-Expressions (MEs) are spontaneous, involuntary facial movements when a person experiences an emotion but deliberately or unconsciously attempts to conceal his or her genuine emotions. Recently, ME recognition has attracted increasing attention due to its potential applications such as clinical diagnosis, business negotiation, interrogations, and security. However, it is expensive to build large scale ME datasets, mainly due to the difficulty of inducing spontaneous MEs. This limits the application of deep learning techniques which require lots of training data. In this paper, we propose a simple, efficient yet robust descriptor called Extended Local Binary Patterns on Three Orthogonal Planes (ELBPTOP) for ME recognition. ELBPTOP consists of three complementary binary descriptors: LBPTOP and two novel ones Radial Difference LBPTOP (RDLBPTOP) and Angular Difference LBPTOP (ADLBPTOP), which explore the local second order information along the radial and angular directions contained in ME video sequences. ELBPTOP is a novel ME descriptor inspired by unique and subtle facial movements. It is computationally efficient and only marginally increases the cost of computing LBPTOP, yet is extremely effective for ME recognition. In addition, by firstly introducing Whitened Principal Component Analysis (WPCA) to ME recognition, we can further obtain more compact and discriminative feature representations, then achieve significantly computational savings. Extensive experimental evaluation on three popular spontaneous ME datasets SMIC, CASME II and SAMM show that our proposed ELBPTOP approach significantly outperforms the previous state-of-the-art on all three single evaluated datasets and achieves promising results on cross-database recognition.Our code will be made available.
Tasks
Published	2019-07-22
URL	https://arxiv.org/abs/1907.09160v2
PDF	https://arxiv.org/pdf/1907.09160v2.pdf
PWC	https://paperswithcode.com/paper/extended-local-binary-patterns-for-efficient
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Military Dog Based Optimizer and its Application to Fake Review


Title	Military Dog Based Optimizer and its Application to Fake Review
Authors	Ashish Kumar Tripathi, Kapil Sharma, Manju Bala
Abstract	Over the last three decades more then sixty meta-heuristic algorithms have been proposed by the various authors. Such algorithms are inspired from physical phenomena, animal behavior or evolutionary concepts. These algorithms have been widely used for solving the various real world optimization problems. Researchers are continuously working to improve the existing algorithms and also proposing new algorithms that are giving competitive results as compared to the existing algorithms present in the literature. In this paper a novel meta heuristic algorithm based on military dogs squad is introduced. The proposed algorithm mimics the searching capability of the trained military dogs. Military dogs have strong smell senses by which they are able to search the suspicious objects like bombs, wildlife scats, currency, or blood as well as they can communicate with each other by their barking. The performance of the proposed algorithm is tested on 17 benchmark functions and compared with five other meta-heuristics namely particle swarm optimization (PSO), multiverse optimizer (MVO), genetic algorithm (GA), probability based learning (PBIL) and evolutionary strategy (ES). The results are validated in terms of mean and standard deviation of the fitness value. The convergence behavior and consistency of the results have been also validated by plotting convergence graphs and BoxPlots. Further the, proposed algorithm is successfully utilized to solve the real world fake review detection problem. The experimental results demonstrate that the proposed algorithm outperforms the other considered algorithms on the majority of performance parameters.
Tasks
Published	2019-09-26
URL	https://arxiv.org/abs/1909.11890v1
PDF	https://arxiv.org/pdf/1909.11890v1.pdf
PWC	https://paperswithcode.com/paper/military-dog-based-optimizer-and-its
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Data-Driven Optimization of Public Transit Schedule


Title	Data-Driven Optimization of Public Transit Schedule
Authors	Sanchita Basak, Fangzhou Sun, Saptarshi Sengupta, Abhishek Dubey
Abstract	Bus transit systems are the backbone of public transportation in the United States. An important indicator of the quality of service in such infrastructures is on-time performance at stops, with published transit schedules playing an integral role governing the level of success of the service. However there are relatively few optimization architectures leveraging stochastic search that focus on optimizing bus timetables with the objective of maximizing probability of bus arrivals at timepoints with delays within desired on-time ranges. In addition to this, there is a lack of substantial research considering monthly and seasonal variations of delay patterns integrated with such optimization strategies. To address these,this paper makes the following contributions to the corpus of studies on transit on-time performance optimization: (a) an unsupervised clustering mechanism is presented which groups months with similar seasonal delay patterns, (b) the problem is formulated as a single-objective optimization task and a greedy algorithm, a genetic algorithm (GA) as well as a particle swarm optimization (PSO) algorithm are employed to solve it, (c) a detailed discussion on empirical results comparing the algorithms are provided and sensitivity analysis on hyper-parameters of the heuristics are presented along with execution times, which will help practitioners looking at similar problems. The analyses conducted are insightful in the local context of improving public transit scheduling in the Nashville metro region as well as informative from a global perspective as an elaborate case study which builds upon the growing corpus of empirical studies using nature-inspired approaches to transit schedule optimization.
Tasks
Published	2019-11-30
URL	https://arxiv.org/abs/1912.02574v1
PDF	https://arxiv.org/pdf/1912.02574v1.pdf
PWC	https://paperswithcode.com/paper/data-driven-optimization-of-public-transit
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Cross-lingual Data Transformation and Combination for Text Classification


Title	Cross-lingual Data Transformation and Combination for Text Classification
Authors	Jun Jiang, Shumao Pang, Xia Zhao, Liwei Wang, Andrew Wen, Hongfang Liu, Qianjin Feng
Abstract	Text classification is a fundamental task for text data mining. In order to train a generalizable model, a large volume of text must be collected. To address data insufficiency, cross-lingual data may occasionally be necessary. Cross-lingual data sources may however suffer from data incompatibility, as text written in different languages can hold distinct word sequences and semantic patterns. Machine translation and word embedding alignment provide an effective way to transform and combine data for cross-lingual data training. To the best of our knowledge, there has been little work done on evaluating how the methodology used to conduct semantic space transformation and data combination affects the performance of classification models trained from cross-lingual resources. In this paper, we systematically evaluated the performance of two commonly used CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network) text classifiers with differing data transformation and combination strategies. Monolingual models were trained from English and French alongside their translated and aligned embeddings. Our results suggested that semantic space transformation may conditionally promote the performance of monolingual models. Bilingual models were trained from a combination of both English and French. Our results indicate that a cross-lingual classification model can significantly benefit from cross-lingual data by learning from translated or aligned embedding spaces.
Tasks	Machine Translation, Text Classification
Published	2019-06-23
URL	https://arxiv.org/abs/1906.09543v1
PDF	https://arxiv.org/pdf/1906.09543v1.pdf
PWC	https://paperswithcode.com/paper/cross-lingual-data-transformation-and
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Framework

Deep Policy Hashing Network with Listwise Supervision


Title	Deep Policy Hashing Network with Listwise Supervision
Authors	Shaoying Wang, Haijiang Lai, Yifan Yang, Jian Yin
Abstract	Deep-networks-based hashing has become a leading approach for large-scale image retrieval, which learns a similarity-preserving network to map similar images to nearby hash codes. The pairwise and triplet losses are two widely used similarity preserving manners for deep hashing. These manners ignore the fact that hashing is a prediction task on the list of binary codes. However, learning deep hashing with listwise supervision is challenging in 1) how to obtain the rank list of whole training set when the batch size of the deep network is always small and 2) how to utilize the listwise supervision. In this paper, we present a novel deep policy hashing architecture with two systems are learned in parallel: a query network and a shared and slowly changing database network. The following three steps are repeated until convergence: 1) the database network encodes all training samples into binary codes to obtain a whole rank list, 2) the query network is trained based on policy learning to maximize a reward that indicates the performance of the whole ranking list of binary codes, e.g., mean average precision (MAP), and 3) the database network is updated as the query network. Extensive evaluations on several benchmark datasets show that the proposed method brings substantial improvements over state-of-the-art hashing methods.
Tasks	Image Retrieval
Published	2019-04-03
URL	http://arxiv.org/abs/1904.01728v1
PDF	http://arxiv.org/pdf/1904.01728v1.pdf
PWC	https://paperswithcode.com/paper/deep-policy-hashing-network-with-listwise
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Framework

Recommendation System-based Upper Confidence Bound for Online Advertising


Title	Recommendation System-based Upper Confidence Bound for Online Advertising
Authors	Nhan Nguyen-Thanh, Dana Marinca, Kinda Khawam, David Rohde, Flavian Vasile, Elena Simona Lohan, Steven Martin, Dominique Quadri
Abstract	In this paper, the method UCB-RS, which resorts to recommendation system (RS) for enhancing the upper-confidence bound algorithm UCB, is presented. The proposed method is used for dealing with non-stationary and large-state spaces multi-armed bandit problems. The proposed method has been targeted to the problem of the product recommendation in the online advertising. Through extensive testing with RecoGym, an OpenAI Gym-based reinforcement learning environment for the product recommendation in online advertising, the proposed method outperforms the widespread reinforcement learning schemes such as $\epsilon$-Greedy, Upper Confidence (UCB1) and Exponential Weights for Exploration and Exploitation (EXP3).
Tasks	Product Recommendation
Published	2019-09-09
URL	https://arxiv.org/abs/1909.04190v1
PDF	https://arxiv.org/pdf/1909.04190v1.pdf
PWC	https://paperswithcode.com/paper/recommendation-system-based-upper-confidence
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Adversarial Domain Adaptation for Stance Detection


Title	Adversarial Domain Adaptation for Stance Detection
Authors	Brian Xu, Mitra Mohtarami, James Glass
Abstract	This paper studies the problem of stance detection which aims to predict the perspective (or stance) of a given document with respect to a given claim. Stance detection is a major component of automated fact checking. As annotating stances in different domains is a tedious and costly task, automatic methods based on machine learning are viable alternatives. In this paper, we focus on adversarial domain adaptation for stance detection where we assume there exists sufficient labeled data in the source domain and limited labeled data in the target domain. Extensive experiments on publicly available datasets show the effectiveness of our domain adaption model in transferring knowledge for accurate stance detection across domains.
Tasks	Domain Adaptation, Stance Detection
Published	2019-02-06
URL	http://arxiv.org/abs/1902.02401v1
PDF	http://arxiv.org/pdf/1902.02401v1.pdf
PWC	https://paperswithcode.com/paper/adversarial-domain-adaptation-for-stance
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