2,137 research outputs found
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μ°.In this thesis, we propose a semi-supervised dictionary learning algorithm that learns representations of only non-outlier data. The presence of outliers in a dataset is a major drawback for dictionary learning, resulting in less than desirable performance in real-world applications. Our adversarial dictionary learning (ADL) algorithm exploits a supervision dataset composed of known outliers. The algorithm penalizes the dictionary expressing the known outliers well. Penalizing the known outliers makes dictionary learning robust to the outliers present in the dataset. The proposed method can handle highly corrupted dataset which cannot be effectively dealt with using conventional robust dictionary learning algorithms. We empirically show the usefulness of our algorithm with extensive experiments on anomaly detection, using both synthetic univariate time-series data and multivariate point data.λ³Έ λ
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μμλ ν¨κ³Όμ μΌλ‘ μ¬μ μ νμ΅ν΄ λΈλ€. μ΄ μ°κ΅¬μμλ μΈκ³΅μ μΈ λ¨λ³λ μκ³μ΄ λ°μ΄ν°μ λ€λ³λ μ λ°μ΄ν°μ λν μ΄μμΉ νμ§ μ€νμ ν΅ν΄ μκ³ λ¦¬μ¦μ μ μ©μ±μ κ²½νμ μΌλ‘ κ²μ¦νλ€.1 Introduction 1
1.1 Related Works 4
1.2 Contributions of This Thesis 5
1.3 Organization 6
2 Sparse Representation and Dictionary Learning 7
2.1 Sparse Representation 7
2.1.1 Problem De nition of Sparse Representation 7
2.1.2 Sparse representation with l0-norm regularization 10
2.1.3 Sparse representation with l1-norm regularization 11
2.1.4 Sparse representation with lp-norm regularization (0 < p < 1) 12
2.2 Dictionary Learning 12
2.2.1 Problem De nition of Dictionary Learning 12
2.2.2 Dictionary Learning Methods 14
3 Adversarial Dictionary Learning 18
3.1 Problem Formulation 18
3.2 Adversarial Loss 19
3.3 Optimization Algorithm 20
4 Experiments 25
4.1 Data Description 26
4.1.1 Univariate Time-series Data 26
4.1.2 Multivariate Point Data 29
4.2 Evaluation Process 30
4.2.1 A Baseline of Anomaly Detection 30
4.2.2 ROC Curve and AUC 34
4.3 Experiment Setting 35
4.4 Results 36
5 Conclusion 43
Bibliography 45
κ΅λ¬Έμ΄λ‘ 50Maste
Image Anomaly Detection and Localization with Position and Neighborhood Information
Anomaly detection and localization are essential in many areas, where
collecting enough anomalous samples for training is almost impossible. To
overcome this difficulty, many existing methods use a pre-trained network to
encode input images and non-parametric modeling to estimate the encoded feature
distribution. In the modeling process, however, they overlook that position and
neighborhood information affect the distribution of normal features. To use the
information, in this paper, the normal distribution is estimated with
conditional probability given neighborhood features, which is modeled with a
multi-layer perceptron network. At the same time, positional information can be
used by building a histogram of representative features at each position. While
existing methods simply resize the anomaly map into the resolution of an input
image, the proposed method uses an additional refine network that is trained
from synthetic anomaly images to perform better interpolation considering the
shape and edge of the input image. For the popular industrial dataset, MVTec AD
benchmark, the experimental results show \textbf{99.52\%} and \textbf{98.91\%}
AUROC scores in anomaly detection and localization, which is state-of-the-art
performance
Kosmos-2: Grounding Multimodal Large Language Models to the World
We introduce Kosmos-2, a Multimodal Large Language Model (MLLM), enabling new
capabilities of perceiving object descriptions (e.g., bounding boxes) and
grounding text to the visual world. Specifically, we represent refer
expressions as links in Markdown, i.e., ``[text span](bounding boxes)'', where
object descriptions are sequences of location tokens. Together with multimodal
corpora, we construct large-scale data of grounded image-text pairs (called
GrIT) to train the model. In addition to the existing capabilities of MLLMs
(e.g., perceiving general modalities, following instructions, and performing
in-context learning), Kosmos-2 integrates the grounding capability into
downstream applications. We evaluate Kosmos-2 on a wide range of tasks,
including (i) multimodal grounding, such as referring expression comprehension,
and phrase grounding, (ii) multimodal referring, such as referring expression
generation, (iii) perception-language tasks, and (iv) language understanding
and generation. This work lays out the foundation for the development of
Embodiment AI and sheds light on the big convergence of language, multimodal
perception, action, and world modeling, which is a key step toward artificial
general intelligence. Data, demo, and pretrained models are available at
https://aka.ms/kosmos-2.Comment: 20 page
Extensible Modeling and Simulation Framework (XMSF) Opportunities for Web-Based Modeling and Simulation
Technical Opportunities Workshop Whitepaper, 14 June 2002Purpose: As the Department of Defense (DoD) is engaged in both warfighting and institutional
transformation for the new millennium, DoD Modeling & Simulation (M&S) also needs to identify
and adopt transformational technologies which provide direct tactical relevance to warfighters.
Because the only software systems that composably scale to worldwide scope utilize the World
Wide Web, it is evident that an extensible Web-based framework shows great promise to scale up
the capabilities of M&S systems to meet the needs of training, analysis, acquisition, and the
operational warfighter. By embracing commercial web technologies as a shared-communications
platform and a ubiquitous-delivery framework, DoD M&S can fully leverage mainstream practices
for enterprise-wide software development
Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding
Computer vision has a great potential to help our daily lives by searching
for lost keys, watering flowers or reminding us to take a pill. To succeed with
such tasks, computer vision methods need to be trained from real and diverse
examples of our daily dynamic scenes. While most of such scenes are not
particularly exciting, they typically do not appear on YouTube, in movies or TV
broadcasts. So how do we collect sufficiently many diverse but boring samples
representing our lives? We propose a novel Hollywood in Homes approach to
collect such data. Instead of shooting videos in the lab, we ensure diversity
by distributing and crowdsourcing the whole process of video creation from
script writing to video recording and annotation. Following this procedure we
collect a new dataset, Charades, with hundreds of people recording videos in
their own homes, acting out casual everyday activities. The dataset is composed
of 9,848 annotated videos with an average length of 30 seconds, showing
activities of 267 people from three continents. Each video is annotated by
multiple free-text descriptions, action labels, action intervals and classes of
interacted objects. In total, Charades provides 27,847 video descriptions,
66,500 temporally localized intervals for 157 action classes and 41,104 labels
for 46 object classes. Using this rich data, we evaluate and provide baseline
results for several tasks including action recognition and automatic
description generation. We believe that the realism, diversity, and casual
nature of this dataset will present unique challenges and new opportunities for
computer vision community
Understanding the Economic Consequences of Shifting Trends in Population Health
The public economic burden of shifting trends in population health remains uncertain. Sustained increases in obesity, diabetes, and other diseases could reduce life expectancy β with a concomitant decrease in the public-sectorβs annuity burden β but these savings may be offset by worsening functional status, which increases health care spending, reduces labor supply, and increases public assistance. Using a microsimulation approach, we quantify the competing public-finance consequences of shifting trends in population health for medical care costs, labor supply, earnings, wealth, tax revenues, and government expenditures (including Social Security and income assistance). Together, the reduction in smoking and the rise in obesity have increased net public-sector liabilities by $430bn, or approximately 4% of the current debt burden. Larger effects are observed for specific public programs: annual spending is 10% higher in the Medicaid program, and 7% higher for Medicare.disability, health care costs, social security, microsimulation
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