Paper Group ANR 473
CNN-CASS: CNN for Classification of Coronary Artery Stenosis Score in MPR Images. FOCUS: Dealing with Label Quality Disparity in Federated Learning. Secure Metric Learning via Differential Pairwise Privacy. OpenGAN: Open Set Generative Adversarial Networks. Combining Offline Causal Inference and Online Bandit Learning for Data Driven Decisions. Bui …
CNN-CASS: CNN for Classification of Coronary Artery Stenosis Score in MPR Images
| Title | CNN-CASS: CNN for Classification of Coronary Artery Stenosis Score in MPR Images |
| Authors | Mariia Dobko, Bohdan Petryshak, Oles Dobosevych |
| Abstract | To decrease patient waiting time for diagnosis of the Coronary Artery Disease, automatic methods are applied to identify its severity using Coronary Computed Tomography Angiography scans or extracted Multiplanar Reconstruction (MPR) images, giving doctors a second-opinion on the priority of each case. The main disadvantage of previous studies is the lack of large set of data that could guarantee their reliability. Another limitation is the usage of handcrafted features requiring manual preprocessing, such as centerline extraction. We overcome both limitations by applying a different automated approach based on ShuffleNet V2 network architecture and testing it on the proposed collected dataset of MPR images, which is bigger than any other used in this field before. We also omit centerline extraction step and train and test our model using whole curved MPR images of 708 and 105 patients, respectively. The model predicts one of three classes: ‘no stenosis’ for normal, ‘non-significant’ - 1-50% of stenosis detected, ‘significant’ - more than 50% of stenosis. We demonstrate model’s interpretability through visualization of the most important features selected by the network. For stenosis score classification, the method shows improved performance comparing to previous works, achieving 80% accuracy on the patient level. Our code is publicly available. |
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| Published | 2020-01-23 |
| URL | https://arxiv.org/abs/2001.08593v1 |
| https://arxiv.org/pdf/2001.08593v1.pdf | |
| PWC | https://paperswithcode.com/paper/cnn-cass-cnn-for-classification-of-coronary |
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FOCUS: Dealing with Label Quality Disparity in Federated Learning
| Title | FOCUS: Dealing with Label Quality Disparity in Federated Learning |
| Authors | Yiqiang Chen, Xiaodong Yang, Xin Qin, Han Yu, Biao Chen, Zhiqi Shen |
| Abstract | Ubiquitous systems with End-Edge-Cloud architecture are increasingly being used in healthcare applications. Federated Learning (FL) is highly useful for such applications, due to silo effect and privacy preserving. Existing FL approaches generally do not account for disparities in the quality of local data labels. However, the clients in ubiquitous systems tend to suffer from label noise due to varying skill-levels, biases or malicious tampering of the annotators. In this paper, we propose Federated Opportunistic Computing for Ubiquitous Systems (FOCUS) to address this challenge. It maintains a small set of benchmark samples on the FL server and quantifies the credibility of the client local data without directly observing them by computing the mutual cross-entropy between performance of the FL model on the local datasets and that of the client local FL model on the benchmark dataset. Then, a credit weighted orchestration is performed to adjust the weight assigned to clients in the FL model based on their credibility values. FOCUS has been experimentally evaluated on both synthetic data and real-world data. The results show that it effectively identifies clients with noisy labels and reduces their impact on the model performance, thereby significantly outperforming existing FL approaches. |
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| Published | 2020-01-29 |
| URL | https://arxiv.org/abs/2001.11359v1 |
| https://arxiv.org/pdf/2001.11359v1.pdf | |
| PWC | https://paperswithcode.com/paper/focus-dealing-with-label-quality-disparity-in |
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Secure Metric Learning via Differential Pairwise Privacy
| Title | Secure Metric Learning via Differential Pairwise Privacy |
| Authors | Jing Li, Yuangang Pan, Yulei Sui, Ivor W. Tsang |
| Abstract | Distance Metric Learning (DML) has drawn much attention over the last two decades. A number of previous works have shown that it performs well in measuring the similarities of individuals given a set of correctly labeled pairwise data by domain experts. These important and precisely-labeled pairwise data are often highly sensitive in real world (e.g., patients similarity). This paper studies, for the first time, how pairwise information can be leaked to attackers during distance metric learning, and develops differential pairwise privacy (DPP), generalizing the definition of standard differential privacy, for secure metric learning. Unlike traditional differential privacy which only applies to independent samples, thus cannot be used for pairwise data, DPP successfully deals with this problem by reformulating the worst case. Specifically, given the pairwise data, we reveal all the involved correlations among pairs in the constructed undirected graph. DPP is then formalized that defines what kind of DML algorithm is private to preserve pairwise data. After that, a case study employing the contrastive loss is exhibited to clarify the details of implementing a DPP-DML algorithm. Particularly, the sensitivity reduction technique is proposed to enhance the utility of the output distance metric. Experiments both on a toy dataset and benchmarks demonstrate that the proposed scheme achieves pairwise data privacy without compromising the output performance much (Accuracy declines less than 0.01 throughout all benchmark datasets when the privacy budget is set at 4). |
| Tasks | Metric Learning |
| Published | 2020-03-30 |
| URL | https://arxiv.org/abs/2003.13413v1 |
| https://arxiv.org/pdf/2003.13413v1.pdf | |
| PWC | https://paperswithcode.com/paper/secure-metric-learning-via-differential |
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OpenGAN: Open Set Generative Adversarial Networks
| Title | OpenGAN: Open Set Generative Adversarial Networks |
| Authors | Luke Ditria, Benjamin J. Meyer, Tom Drummond |
| Abstract | Many existing conditional Generative Adversarial Networks (cGANs) are limited to conditioning on pre-defined and fixed class-level semantic labels or attributes. We propose an open set GAN architecture (OpenGAN) that is conditioned per-input sample with a feature embedding drawn from a metric space. Using a state-of-the-art metric learning model that encodes both class-level and fine-grained semantic information, we are able to generate samples that are semantically similar to a given source image. The semantic information extracted by the metric learning model transfers to out-of-distribution novel classes, allowing the generative model to produce samples that are outside of the training distribution. We show that our proposed method is able to generate 256$\times$256 resolution images from novel classes that are of similar visual quality to those from the training classes. In lieu of a source image, we demonstrate that random sampling of the metric space also results in high-quality samples. We show that interpolation in the feature space and latent space results in semantically and visually plausible transformations in the image space. Finally, the usefulness of the generated samples to the downstream task of data augmentation is demonstrated. We show that classifier performance can be significantly improved by augmenting the training data with OpenGAN samples on classes that are outside of the GAN training distribution. |
| Tasks | Data Augmentation, Metric Learning |
| Published | 2020-03-18 |
| URL | https://arxiv.org/abs/2003.08074v1 |
| https://arxiv.org/pdf/2003.08074v1.pdf | |
| PWC | https://paperswithcode.com/paper/opengan-open-set-generative-adversarial |
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Combining Offline Causal Inference and Online Bandit Learning for Data Driven Decisions
| Title | Combining Offline Causal Inference and Online Bandit Learning for Data Driven Decisions |
| Authors | Li Ye, Yishi Lin, Hong Xie, John C. S. Lui |
| Abstract | A fundamental question for companies is: How to make good decisions with the increasing amount of logged data?. Currently, companies are doing online tests (e.g. A/B tests) before making decisions. However, online tests can be expensive because testing inferior decisions hurt users’ experiences. On the other hand, offline causal inference analyzes logged data alone to make decisions, but once a wrong decision is made by the offline causal inference, this wrong decision will continuously to hurt all users’ experience. In this paper, we unify offline causal inference and online bandit learning to make the right decision. Our framework is flexible to incorporate various causal inference methods (e.g. matching, weighting) and online bandit methods (e.g. UCB, LinUCB). For these novel combination of algorithms, we derive theoretical bounds on the decision maker’s “regret” compared to its optimal decision. We also derive the first regret bound for forest-based online bandit algorithms. Experiments on synthetic data show that our algorithms outperform methods that use only the logged data or only the online feedbacks. |
| Tasks | Causal Inference |
| Published | 2020-01-16 |
| URL | https://arxiv.org/abs/2001.05699v1 |
| https://arxiv.org/pdf/2001.05699v1.pdf | |
| PWC | https://paperswithcode.com/paper/combining-offline-causal-inference-and-online |
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Building Computationally Efficient and Well-Generalizing Person Re-Identification Models with Metric Learning
| Title | Building Computationally Efficient and Well-Generalizing Person Re-Identification Models with Metric Learning |
| Authors | Vladislav Sovrasov, Dmitry Sidnev |
| Abstract | This work considers the problem of domain shift in person re-identification.Being trained on one dataset, a re-identification model usually performs much worse on unseen data. Partially this gap is caused by the relatively small scale of person re-identification datasets (compared to face recognition ones, for instance), but it is also related to training objectives. We propose to use the metric learning objective, namely AM-Softmax loss, and some additional training practices to build well-generalizing, yet, computationally efficient models. We use recently proposed Omni-Scale Network (OSNet) architecture combined with several training tricks and architecture adjustments to obtain state-of-the art results in cross-domain generalization problem on a large-scale MSMT17 dataset in three setups: MSMT17-all->DukeMTMC, MSMT17-train->Market1501 and MSMT17-all->Market1501. |
| Tasks | Domain Generalization, Face Recognition, Metric Learning, Person Re-Identification |
| Published | 2020-03-17 |
| URL | https://arxiv.org/abs/2003.07618v1 |
| https://arxiv.org/pdf/2003.07618v1.pdf | |
| PWC | https://paperswithcode.com/paper/building-computationally-efficient-and-well |
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Vec2Face: Unveil Human Faces from their Blackbox Features in Face Recognition
| Title | Vec2Face: Unveil Human Faces from their Blackbox Features in Face Recognition |
| Authors | Chi Nhan Duong, Thanh-Dat Truong, Kha Gia Quach, Hung Bui, Kaushik Roy, Khoa Luu |
| Abstract | Unveiling face images of a subject given his/her high-level representations extracted from a blackbox Face Recognition engine is extremely challenging. It is because the limitations of accessible information from that engine including its structure and uninterpretable extracted features. This paper presents a novel generative structure with Bijective Metric Learning, namely Bijective Generative Adversarial Networks in a Distillation framework (DiBiGAN), for synthesizing faces of an identity given that person’s features. In order to effectively address this problem, this work firstly introduces a bijective metric so that the distance measurement and metric learning process can be directly adopted in image domain for an image reconstruction task. Secondly, a distillation process is introduced to maximize the information exploited from the blackbox face recognition engine. Then a Feature-Conditional Generator Structure with Exponential Weighting Strategy is presented for a more robust generator that can synthesize realistic faces with ID preservation. Results on several benchmarking datasets including CelebA, LFW, AgeDB, CFP-FP against matching engines have demonstrated the effectiveness of DiBiGAN on both image realism and ID preservation properties. |
| Tasks | Face Recognition, Image Reconstruction, Metric Learning |
| Published | 2020-03-16 |
| URL | https://arxiv.org/abs/2003.06958v1 |
| https://arxiv.org/pdf/2003.06958v1.pdf | |
| PWC | https://paperswithcode.com/paper/vec2face-unveil-human-faces-from-their |
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Deep S$^3$PR: Simultaneous Source Separation and Phase Retrieval Using Deep Generative Models
| Title | Deep S$^3$PR: Simultaneous Source Separation and Phase Retrieval Using Deep Generative Models |
| Authors | Christopher A. Metzler, Gordon Wetzstein |
| Abstract | This paper introduces and solves the simultaneous source separation and phase retrieval (S$^3$PR) problem. S$^3$PR shows up in a number application domains, most notably computational optics, where one has multiple independent coherent sources whose phase is difficult to measure. In general, S$^3$PR is highly under-determined, non-convex, and difficult to solve. In this work, we demonstrate that by restricting the solutions to lie in the range of a deep generative model, we can constrain the search space sufficiently to solve S$^3$PR. |
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| Published | 2020-02-14 |
| URL | https://arxiv.org/abs/2002.05856v1 |
| https://arxiv.org/pdf/2002.05856v1.pdf | |
| PWC | https://paperswithcode.com/paper/deep-s3pr-simultaneous-source-separation-and |
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Prob2Vec: Mathematical Semantic Embedding for Problem Retrieval in Adaptive Tutoring
| Title | Prob2Vec: Mathematical Semantic Embedding for Problem Retrieval in Adaptive Tutoring |
| Authors | Du Su, Ali Yekkehkhany, Yi Lu, Wenmiao Lu |
| Abstract | We propose a new application of embedding techniques for problem retrieval in adaptive tutoring. The objective is to retrieve problems whose mathematical concepts are similar. There are two challenges: First, like sentences, problems helpful to tutoring are never exactly the same in terms of the underlying concepts. Instead, good problems mix concepts in innovative ways, while still displaying continuity in their relationships. Second, it is difficult for humans to determine a similarity score that is consistent across a large enough training set. We propose a hierarchical problem embedding algorithm, called Prob2Vec, that consists of abstraction and embedding steps. Prob2Vec achieves 96.88% accuracy on a problem similarity test, in contrast to 75% from directly applying state-of-the-art sentence embedding methods. It is interesting that Prob2Vec is able to distinguish very fine-grained differences among problems, an ability humans need time and effort to acquire. In addition, the sub-problem of concept labeling with imbalanced training data set is interesting in its own right. It is a multi-label problem suffering from dimensionality explosion, which we propose ways to ameliorate. We propose the novel negative pre-training algorithm that dramatically reduces false negative and positive ratios for classification, using an imbalanced training data set. |
| Tasks | Sentence Embedding |
| Published | 2020-03-21 |
| URL | https://arxiv.org/abs/2003.10838v1 |
| https://arxiv.org/pdf/2003.10838v1.pdf | |
| PWC | https://paperswithcode.com/paper/prob2vec-mathematical-semantic-embedding-for-1 |
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Machine Learning-based Approach for Depression Detection in Twitter Using Content and Activity Features
| Title | Machine Learning-based Approach for Depression Detection in Twitter Using Content and Activity Features |
| Authors | Hatoon S. AlSagri, Mourad Ykhlef |
| Abstract | Social media channels, such as Facebook, Twitter, and Instagram, have altered our world forever. People are now increasingly connected than ever and reveal a sort of digital persona. Although social media certainly has several remarkable features, the demerits are undeniable as well. Recent studies have indicated a correlation between high usage of social media sites and increased depression. The present study aims to exploit machine learning techniques for detecting a probable depressed Twitter user based on both, his/her network behavior and tweets. For this purpose, we trained and tested classifiers to distinguish whether a user is depressed or not using features extracted from his/ her activities in the network and tweets. The results showed that the more features are used, the higher are the accuracy and F-measure scores in detecting depressed users. This method is a data-driven, predictive approach for early detection of depression or other mental illnesses. This study’s main contribution is the exploration part of the features and its impact on detecting the depression level. |
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| Published | 2020-03-09 |
| URL | https://arxiv.org/abs/2003.04763v1 |
| https://arxiv.org/pdf/2003.04763v1.pdf | |
| PWC | https://paperswithcode.com/paper/machine-learning-based-approach-for |
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Application of Deep Q-Network in Portfolio Management
| Title | Application of Deep Q-Network in Portfolio Management |
| Authors | Ziming Gao, Yuan Gao, Yi Hu, Zhengyong Jiang, Jionglong Su |
| Abstract | Machine Learning algorithms and Neural Networks are widely applied to many different areas such as stock market prediction, face recognition and population analysis. This paper will introduce a strategy based on the classic Deep Reinforcement Learning algorithm, Deep Q-Network, for portfolio management in stock market. It is a type of deep neural network which is optimized by Q Learning. To make the DQN adapt to financial market, we first discretize the action space which is defined as the weight of portfolio in different assets so that portfolio management becomes a problem that Deep Q-Network can solve. Next, we combine the Convolutional Neural Network and dueling Q-net to enhance the recognition ability of the algorithm. Experimentally, we chose five lowrelevant American stocks to test the model. The result demonstrates that the DQN based strategy outperforms the ten other traditional strategies. The profit of DQN algorithm is 30% more than the profit of other strategies. Moreover, the Sharpe ratio associated with Max Drawdown demonstrates that the risk of policy made with DQN is the lowest. |
| Tasks | Face Recognition, Q-Learning, Stock Market Prediction |
| Published | 2020-03-13 |
| URL | https://arxiv.org/abs/2003.06365v1 |
| https://arxiv.org/pdf/2003.06365v1.pdf | |
| PWC | https://paperswithcode.com/paper/application-of-deep-q-network-in-portfolio |
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Variable-Viewpoint Representations for 3D Object Recognition
| Title | Variable-Viewpoint Representations for 3D Object Recognition |
| Authors | Tengyu Ma, Joel Michelson, James Ainooson, Deepayan Sanyal, Xiaohan Wang, Maithilee Kunda |
| Abstract | For the problem of 3D object recognition, researchers using deep learning methods have developed several very different input representations, including “multi-view” snapshots taken from discrete viewpoints around an object, as well as “spherical” representations consisting of a dense map of essentially ray-traced samples of the object from all directions. These representations offer trade-offs in terms of what object information is captured and to what degree of detail it is captured, but it is not clear how to measure these information trade-offs since the two types of representations are so different. We demonstrate that both types of representations in fact exist at two extremes of a common representational continuum, essentially choosing to prioritize either the number of views of an object or the pixels (i.e., field of view) allotted per view. We identify interesting intermediate representations that lie at points in between these two extremes, and we show, through systematic empirical experiments, how accuracy varies along this continuum as a function of input information as well as the particular deep learning architecture that is used. |
| Tasks | 3D Object Recognition, Object Recognition |
| Published | 2020-02-08 |
| URL | https://arxiv.org/abs/2002.03131v1 |
| https://arxiv.org/pdf/2002.03131v1.pdf | |
| PWC | https://paperswithcode.com/paper/variable-viewpoint-representations-for-3d |
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Scalable Gradients for Stochastic Differential Equations
| Title | Scalable Gradients for Stochastic Differential Equations |
| Authors | Xuechen Li, Ting-Kam Leonard Wong, Ricky T. Q. Chen, David Duvenaud |
| Abstract | The adjoint sensitivity method scalably computes gradients of solutions to ordinary differential equations. We generalize this method to stochastic differential equations, allowing time-efficient and constant-memory computation of gradients with high-order adaptive solvers. Specifically, we derive a stochastic differential equation whose solution is the gradient, a memory-efficient algorithm for caching noise, and conditions under which numerical solutions converge. In addition, we combine our method with gradient-based stochastic variational inference for latent stochastic differential equations. We use our method to fit stochastic dynamics defined by neural networks, achieving competitive performance on a 50-dimensional motion capture dataset. |
| Tasks | Motion Capture |
| Published | 2020-01-05 |
| URL | https://arxiv.org/abs/2001.01328v4 |
| https://arxiv.org/pdf/2001.01328v4.pdf | |
| PWC | https://paperswithcode.com/paper/scalable-gradients-for-stochastic |
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An Efficient Framework for Automated Screening of Clinically Significant Macular Edema
| Title | An Efficient Framework for Automated Screening of Clinically Significant Macular Edema |
| Authors | Renoh Johnson Chalakkal, Faizal Hafiz, Waleed Abdulla, Akshya Swain |
| Abstract | The present study proposes a new approach to automated screening of Clinically Significant Macular Edema (CSME) and addresses two major challenges associated with such screenings, i.e., exudate segmentation and imbalanced datasets. The proposed approach replaces the conventional exudate segmentation based feature extraction by combining a pre-trained deep neural network with meta-heuristic feature selection. A feature space over-sampling technique is being used to overcome the effects of skewed datasets and the screening is accomplished by a k-NN based classifier. The role of each data-processing step (e.g., class balancing, feature selection) and the effects of limiting the region-of-interest to fovea on the classification performance are critically analyzed. Finally, the selection and implication of operating point on Receiver Operating Characteristic curve are discussed. The results of this study convincingly demonstrate that by following these fundamental practices of machine learning, a basic k-NN based classifier could effectively accomplish the CSME screening. |
| Tasks | Feature Selection |
| Published | 2020-01-20 |
| URL | https://arxiv.org/abs/2001.07002v1 |
| https://arxiv.org/pdf/2001.07002v1.pdf | |
| PWC | https://paperswithcode.com/paper/an-efficient-framework-for-automated |
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Behavior Planning For Connected Autonomous Vehicles Using Feedback Deep Reinforcement Learning
| Title | Behavior Planning For Connected Autonomous Vehicles Using Feedback Deep Reinforcement Learning |
| Authors | Songyang Han, Fei Miao |
| Abstract | With the development of communication technologies, connected autonomous vehicles (CAVs) can share information with each other. Besides basic safety messages, they can also share their future plan. We propose a behavior planning method for CAVs to decide whether to change lane or keep lane based on the information received from neighbors and a policy learned by deep reinforcement learning (DRL). Our state design based on shared information is scalable to the number of vehicles. The proposed feedback deep Q-learning algorithms integrate the policy learning process with a continuous state space controller, which in turn gives feedback about actions and rewards to the learning process. We design both centralized and distributed DRL algorithms. In experiments, our behavior planning method can help increase traffic flow and driving comfort compared with a traditional rule-based control method. It also shows the distributed learning result is comparable to the centralized learning result, which reveals the possibility of improving the policy of behavior planning online. We also validate our algorithm in a more complicated scenario where there are two road closures on a freeway. |
| Tasks | Autonomous Vehicles, Q-Learning |
| Published | 2020-03-09 |
| URL | https://arxiv.org/abs/2003.04371v1 |
| https://arxiv.org/pdf/2003.04371v1.pdf | |
| PWC | https://paperswithcode.com/paper/behavior-planning-for-connected-autonomous |
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