April 2, 2020

3157 words 15 mins read

Paper Group ANR 214

Paper Group ANR 214

Neighborhood-based Pooling for Population-level Label Distribution Learning. Automatic phantom test pattern classification through transfer learning with deep neural networks. SUPAID: A Rule mining based method for automatic rollout decision aid for supervisors in fleet management systems. Predicting Semantic Map Representations from Images using P …

Neighborhood-based Pooling for Population-level Label Distribution Learning

Title Neighborhood-based Pooling for Population-level Label Distribution Learning
Authors Tharindu Cyril Weerasooriya, Tong Liu, Christopher M. Homan
Abstract Supervised machine learning often requires human-annotated data. While annotator disagreement is typically interpreted as evidence of noise, population-level label distribution learning (PLDL) treats the collection of annotations for each data item as a sample of the opinions of a population of human annotators, among whom disagreement may be proper and expected, even with no noise present. From this perspective, a typical training set may contain a large number of very small-sized samples, one for each data item, none of which, by itself, is large enough to be considered representative of the underlying population’s beliefs about that item. We propose an algorithmic framework and new statistical tests for PLDL that account for sampling size. We apply them to previously proposed methods for sharing labels across similar data items. We also propose new approaches for label sharing, which we call neighborhood-based pooling.
Published 2020-03-16
URL https://arxiv.org/abs/2003.07406v1
PDF https://arxiv.org/pdf/2003.07406v1.pdf
PWC https://paperswithcode.com/paper/neighborhood-based-pooling-for-population

Automatic phantom test pattern classification through transfer learning with deep neural networks

Title Automatic phantom test pattern classification through transfer learning with deep neural networks
Authors Rafael B. Fricks, Justin Solomon, Ehsan Samei
Abstract Imaging phantoms are test patterns used to measure image quality in computer tomography (CT) systems. A new phantom platform (Mercury Phantom, Gammex) provides test patterns for estimating the task transfer function (TTF) or noise power spectrum (NPF) and simulates different patient sizes. Determining which image slices are suitable for analysis currently requires manual annotation of these patterns by an expert, as subtle defects may make an image unsuitable for measurement. We propose a method of automatically classifying these test patterns in a series of phantom images using deep learning techniques. By adapting a convolutional neural network based on the VGG19 architecture with weights trained on ImageNet, we use transfer learning to produce a classifier for this domain. The classifier is trained and evaluated with over 3,500 phantom images acquired at a university medical center. Input channels for color images are successfully adapted to convey contextual information for phantom images. A series of ablation studies are employed to verify design aspects of the classifier and evaluate its performance under varying training conditions. Our solution makes extensive use of image augmentation to produce a classifier that accurately classifies typical phantom images with 98% accuracy, while maintaining as much as 86% accuracy when the phantom is improperly imaged.
Tasks Image Augmentation, Transfer Learning
Published 2020-01-22
URL https://arxiv.org/abs/2001.08189v1
PDF https://arxiv.org/pdf/2001.08189v1.pdf
PWC https://paperswithcode.com/paper/automatic-phantom-test-pattern-classification

SUPAID: A Rule mining based method for automatic rollout decision aid for supervisors in fleet management systems

Title SUPAID: A Rule mining based method for automatic rollout decision aid for supervisors in fleet management systems
Authors Sahil Manchanda, Arun Rajkumar, Simarjot Kaur, Narayanan Unny
Abstract The decision to rollout a vehicle is critical to fleet management companies as wrong decisions can lead to additional cost of maintenance and failures during journey. With the availability of large amount of data and advancement of machine learning techniques, the rollout decisions of a supervisor can be effectively automated and the mistakes in decisions made by the supervisor learnt. In this paper, we propose a novel learning algorithm SUPAID which under a natural ‘one-way efficiency’ assumption on the supervisor, uses a rule mining approach to rank the vehicles based on their roll-out feasibility thus helping prevent the supervisor from makingerroneous decisions. Our experimental results on real data from a public transit agency from a city in U.S show that the proposed method SUPAID can result in significant cost savings.
Published 2020-01-10
URL https://arxiv.org/abs/2001.03386v2
PDF https://arxiv.org/pdf/2001.03386v2.pdf
PWC https://paperswithcode.com/paper/supaid-a-rule-mining-based-method-for

Predicting Semantic Map Representations from Images using Pyramid Occupancy Networks

Title Predicting Semantic Map Representations from Images using Pyramid Occupancy Networks
Authors Thomas Roddick, Roberto Cipolla
Abstract Autonomous vehicles commonly rely on highly detailed birds-eye-view maps of their environment, which capture both static elements of the scene such as road layout as well as dynamic elements such as other cars and pedestrians. Generating these map representations on the fly is a complex multi-stage process which incorporates many important vision-based elements, including ground plane estimation, road segmentation and 3D object detection. In this work we present a simple, unified approach for estimating maps directly from monocular images using a single end-to-end deep learning architecture. For the maps themselves we adopt a semantic Bayesian occupancy grid framework, allowing us to trivially accumulate information over multiple cameras and timesteps. We demonstrate the effectiveness of our approach by evaluating against several challenging baselines on the NuScenes and Argoverse datasets, and show that we are able to achieve a relative improvement of 9.1% and 22.3% respectively compared to the best-performing existing method.
Tasks 3D Object Detection, Autonomous Vehicles, Object Detection
Published 2020-03-30
URL https://arxiv.org/abs/2003.13402v1
PDF https://arxiv.org/pdf/2003.13402v1.pdf
PWC https://paperswithcode.com/paper/predicting-semantic-map-representations-from

Combining Visible and Infrared Spectrum Imagery using Machine Learning for Small Unmanned Aerial System Detection

Title Combining Visible and Infrared Spectrum Imagery using Machine Learning for Small Unmanned Aerial System Detection
Authors Vinicius G. Goecks, Grayson Woods, Niladri Das, John Valasek
Abstract Advances in machine learning and deep neural networks for object detection, coupled with lower cost and power requirements of cameras, led to promising vision-based solutions for sUAS detection. However, solely relying on the visible spectrum has previously led to reliability issues in low contrast scenarios such as sUAS flying below the treeline and against bright sources of light. Alternatively, due to the relatively high heat signatures emitted from sUAS during flight, a long-wave infrared (LWIR) sensor is able to produce images that clearly contrast the sUAS from its background. However, compared to widely available visible spectrum sensors, LWIR sensors have lower resolution and may produce more false positives when exposed to birds or other heat sources. This research work proposes combining the advantages of the LWIR and visible spectrum sensors using machine learning for vision-based detection of sUAS. Utilizing the heightened background contrast from the LWIR sensor combined and synchronized with the relatively increased resolution of the visible spectrum sensor, a deep learning model was trained to detect the sUAS through previously difficult environments. More specifically, the approach demonstrated effective detection of multiple sUAS flying above and below the treeline, in the presence of heat sources, and glare from the sun. Our approach achieved a detection rate of 71.2 +- 8.3%, improving by 69% when compared to LWIR and by 30.4% when visible spectrum alone, and achieved false alarm rate of 2.7 +- 2.6%, decreasing by 74.1% and by 47.1% when compared to LWIR and visible spectrum alone, respectively, on average, for single and multiple drone scenarios, controlled for the same confidence metric of the machine learning object detector of at least 50%. Videos of the solution’s performance can be seen at https://sites.google.com/view/tamudrone-spie2020/.
Tasks Object Detection
Published 2020-03-27
URL https://arxiv.org/abs/2003.12638v1
PDF https://arxiv.org/pdf/2003.12638v1.pdf
PWC https://paperswithcode.com/paper/combining-visible-and-infrared-spectrum

Pseudo-Labeling for Small Lesion Detection on Diabetic Retinopathy Images

Title Pseudo-Labeling for Small Lesion Detection on Diabetic Retinopathy Images
Authors Qilei Chen, Ping Liu, Jing Ni, Yu Cao, Benyuan Liu, Honggang Zhang
Abstract Diabetic retinopathy (DR) is a primary cause of blindness in working-age people worldwide. About 3 to 4 million people with diabetes become blind because of DR every year. Diagnosis of DR through color fundus images is a common approach to mitigate such problem. However, DR diagnosis is a difficult and time consuming task, which requires experienced clinicians to identify the presence and significance of many small features on high resolution images. Convolutional Neural Network (CNN) has proved to be a promising approach for automatic biomedical image analysis recently. In this work, we investigate lesion detection on DR fundus images with CNN-based object detection methods. Lesion detection on fundus images faces two unique challenges. The first one is that our dataset is not fully labeled, i.e., only a subset of all lesion instances are marked. Not only will these unlabeled lesion instances not contribute to the training of the model, but also they will be mistakenly counted as false negatives, leading the model move to the opposite direction. The second challenge is that the lesion instances are usually very small, making them difficult to be found by normal object detectors. To address the first challenge, we introduce an iterative training algorithm for the semi-supervised method of pseudo-labeling, in which a considerable number of unlabeled lesion instances can be discovered to boost the performance of the lesion detector. For the small size targets problem, we extend both the input size and the depth of feature pyramid network (FPN) to produce a large CNN feature map, which can preserve the detail of small lesions and thus enhance the effectiveness of the lesion detector. The experimental results show that our proposed methods significantly outperform the baselines.
Tasks Object Detection
Published 2020-03-26
URL https://arxiv.org/abs/2003.12040v1
PDF https://arxiv.org/pdf/2003.12040v1.pdf
PWC https://paperswithcode.com/paper/pseudo-labeling-for-small-lesion-detection-on

Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation

Title Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation
Authors Necati Cihan Camgoz, Oscar Koller, Simon Hadfield, Richard Bowden
Abstract Prior work on Sign Language Translation has shown that having a mid-level sign gloss representation (effectively recognizing the individual signs) improves the translation performance drastically. In fact, the current state-of-the-art in translation requires gloss level tokenization in order to work. We introduce a novel transformer based architecture that jointly learns Continuous Sign Language Recognition and Translation while being trainable in an end-to-end manner. This is achieved by using a Connectionist Temporal Classification (CTC) loss to bind the recognition and translation problems into a single unified architecture. This joint approach does not require any ground-truth timing information, simultaneously solving two co-dependant sequence-to-sequence learning problems and leads to significant performance gains. We evaluate the recognition and translation performances of our approaches on the challenging RWTH-PHOENIX-Weather-2014T (PHOENIX14T) dataset. We report state-of-the-art sign language recognition and translation results achieved by our Sign Language Transformers. Our translation networks outperform both sign video to spoken language and gloss to spoken language translation models, in some cases more than doubling the performance (9.58 vs. 21.80 BLEU-4 Score). We also share new baseline translation results using transformer networks for several other text-to-text sign language translation tasks.
Tasks Sign Language Recognition, Sign Language Translation, Tokenization
Published 2020-03-30
URL https://arxiv.org/abs/2003.13830v1
PDF https://arxiv.org/pdf/2003.13830v1.pdf
PWC https://paperswithcode.com/paper/sign-language-transformers-joint-end-to-end

Transfer Learning using Neural Ordinary Differential Equations

Title Transfer Learning using Neural Ordinary Differential Equations
Authors Rajath S, Sumukh Aithal K, Natarajan Subramanyam
Abstract A concept of using Neural Ordinary Differential Equations(NODE) for Transfer Learning has been introduced. In this paper we use the EfficientNets to explore transfer learning on CIFAR-10 dataset. We use NODE for fine-tuning our model. Using NODE for fine tuning provides more stability during training and validation.These continuous depth blocks can also have a trade off between numerical precision and speed .Using Neural ODEs for transfer learning has resulted in much stable convergence of the loss function.
Tasks Transfer Learning
Published 2020-01-21
URL https://arxiv.org/abs/2001.07342v1
PDF https://arxiv.org/pdf/2001.07342v1.pdf
PWC https://paperswithcode.com/paper/transfer-learning-using-neural-ordinary

PENet: Object Detection using Points Estimation in Aerial Images

Title PENet: Object Detection using Points Estimation in Aerial Images
Authors Ziyang Tang, Xiang Liu, Guangyu Shen, Baijian Yang
Abstract Aerial imagery has been increasingly adopted in mission-critical tasks, such as traffic surveillance, smart cities, and disaster assistance. However, identifying objects from aerial images faces the following challenges: 1) objects of interests are often too small and too dense relative to the images; 2) objects of interests are often in different relative sizes; and 3) the number of objects in each category is imbalanced. A novel network structure, Points Estimated Network (PENet), is proposed in this work to answer these challenges. PENet uses a Mask Resampling Module (MRM) to augment the imbalanced datasets, a coarse anchor-free detector (CPEN) to effectively predict the center points of the small object clusters, and a fine anchor-free detector FPEN to locate the precise positions of the small objects. An adaptive merge algorithm Non-maximum Merge (NMM) is implemented in CPEN to address the issue of detecting dense small objects, and a hierarchical loss is defined in FPEN to further improve the classification accuracy. Our extensive experiments on aerial datasets visDrone and UAVDT showed that PENet achieved higher precision results than existing state-of-the-art approaches. Our best model achieved 8.7% improvement on visDrone and 20.3% on UAVDT.
Tasks Object Detection
Published 2020-01-22
URL https://arxiv.org/abs/2001.08247v1
PDF https://arxiv.org/pdf/2001.08247v1.pdf
PWC https://paperswithcode.com/paper/penet-object-detection-using-points

A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications

Title A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
Authors Jie Gui, Zhenan Sun, Yonggang Wen, Dacheng Tao, Jieping Ye
Abstract Generative adversarial networks (GANs) are a hot research topic recently. GANs have been widely studied since 2014, and a large number of algorithms have been proposed. However, there is few comprehensive study explaining the connections among different GANs variants, and how they have evolved. In this paper, we attempt to provide a review on various GANs methods from the perspectives of algorithms, theory, and applications. Firstly, the motivations, mathematical representations, and structure of most GANs algorithms are introduced in details. Furthermore, GANs have been combined with other machine learning algorithms for specific applications, such as semi-supervised learning, transfer learning, and reinforcement learning. This paper compares the commonalities and differences of these GANs methods. Secondly, theoretical issues related to GANs are investigated. Thirdly, typical applications of GANs in image processing and computer vision, natural language processing, music, speech and audio, medical field, and data science are illustrated. Finally, the future open research problems for GANs are pointed out.
Tasks Transfer Learning
Published 2020-01-20
URL https://arxiv.org/abs/2001.06937v1
PDF https://arxiv.org/pdf/2001.06937v1.pdf
PWC https://paperswithcode.com/paper/a-review-on-generative-adversarial-networks

Heterogeneous Transfer Learning in Ensemble Clustering

Title Heterogeneous Transfer Learning in Ensemble Clustering
Authors Vladimir Berikov
Abstract This work proposes an ensemble clustering method using transfer learning approach. We consider a clustering problem, in which in addition to data under consideration, “similar” labeled data are available. The datasets can be described with different features. The method is based on constructing meta-features which describe structural characteristics of data, and their transfer from source to target domain. An experimental study of the method using Monte Carlo modeling has confirmed its efficiency. In comparison with other similar methods, the proposed one is able to work under arbitrary feature descriptions of source and target domains; it has smaller complexity.
Tasks Transfer Learning
Published 2020-01-20
URL https://arxiv.org/abs/2001.07155v1
PDF https://arxiv.org/pdf/2001.07155v1.pdf
PWC https://paperswithcode.com/paper/heterogeneous-transfer-learning-in-ensemble

Short Text Classification via Term Graph

Title Short Text Classification via Term Graph
Authors Wei Pang
Abstract Short text classi cation is a method for classifying short sentence with prede ned labels. However, short text is limited in shortness in text length that leads to a challenging problem of sparse features. Most of existing methods treat each short sentences as independently and identically distributed (IID), local context only in the sentence itself is focused and the relational information between sentences are lost. To overcome these limitations, we propose a PathWalk model that combine the strength of graph networks and short sentences to solve the sparseness of short text. Experimental results on four different available datasets show that our PathWalk method achieves the state-of-the-art results, demonstrating the efficiency and robustness of graph networks for short text classification.
Tasks Text Classification
Published 2020-01-20
URL https://arxiv.org/abs/2001.10338v1
PDF https://arxiv.org/pdf/2001.10338v1.pdf
PWC https://paperswithcode.com/paper/short-text-classification-via-term-graph

Differential Evolution with Individuals Redistribution for Real Parameter Single Objective Optimization

Title Differential Evolution with Individuals Redistribution for Real Parameter Single Objective Optimization
Authors Chengjun Li, Yang Li
Abstract Differential Evolution (DE) is quite powerful for real parameter single objective optimization. However, the ability of extending or changing search area when falling into a local optimum is still required to be developed in DE for accommodating extremely complicated fitness landscapes with a huge number of local optima. We propose a new flow of DE, termed DE with individuals redistribution, in which a process of individuals redistribution will be called when progress on fitness is low for generations. In such a process, mutation and crossover are standardized, while trial vectors are all kept in selection. Once diversity exceeds a predetermined threshold, our opposition replacement is executed, then algorithm behavior returns to original mode. In our experiments based on two benchmark test suites, we apply individuals redistribution in ten DE algorithms. Versions of the ten DE algorithms based on individuals redistribution are compared with not only original version but also version based on complete restart, where individuals redistribution and complete restart are based on the same entry criterion. Experimental results indicate that, for most of the DE algorithms, version based on individuals redistribution performs better than both original version and version based on complete restart.
Published 2020-03-01
URL https://arxiv.org/abs/2003.00439v1
PDF https://arxiv.org/pdf/2003.00439v1.pdf
PWC https://paperswithcode.com/paper/differential-evolution-with-individuals

Reinforcement Learning for Electricity Network Operation

Title Reinforcement Learning for Electricity Network Operation
Authors Adrian Kelly, Aidan O’Sullivan, Patrick de Mars, Antoine Marot
Abstract This paper presents the background material required for the Learning to Run Power Networks Challenge. The challenge is focused on using Reinforcement Learning to train an agent to manage the real-time operations of a power grid, balancing power flows and making interventions to maintain stability. We present an introduction to power systems targeted at the machine learning community and an introduction to reinforcement learning targeted at the power systems community. This is to enable and encourage broader participation in the challenge and collaboration between these two communities.
Published 2020-03-16
URL https://arxiv.org/abs/2003.07339v1
PDF https://arxiv.org/pdf/2003.07339v1.pdf
PWC https://paperswithcode.com/paper/reinforcement-learning-for-electricity

Post-training Quantization with Multiple Points: Mixed Precision without Mixed Precision

Title Post-training Quantization with Multiple Points: Mixed Precision without Mixed Precision
Authors Xingchao Liu, Mao Ye, Dengyong Zhou, Qiang Liu
Abstract We consider the post-training quantization problem, which discretizes the weights of pre-trained deep neural networks without re-training the model. We propose multipoint quantization, a quantization method that approximates a full-precision weight vector using a linear combination of multiple vectors of low-bit numbers; this is in contrast to typical quantization methods that approximate each weight using a single low precision number. Computationally, we construct the multipoint quantization with an efficient greedy selection procedure, and adaptively decides the number of low precision points on each quantized weight vector based on the error of its output. This allows us to achieve higher precision levels for important weights that greatly influence the outputs, yielding an ‘effect of mixed precision’ but without physical mixed precision implementations (which requires specialized hardware accelerators). Empirically, our method can be implemented by common operands, bringing almost no memory and computation overhead. We show that our method outperforms a range of state-of-the-art methods on ImageNet classification and it can be generalized to more challenging tasks like PASCAL VOC object detection.
Tasks Object Detection, Quantization
Published 2020-02-20
URL https://arxiv.org/abs/2002.09049v2
PDF https://arxiv.org/pdf/2002.09049v2.pdf
PWC https://paperswithcode.com/paper/post-training-quantization-with-multiple
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