pwc-archive/papers-with-abstracts huggingface dataset:
Triplet Loss for Knowledge Distillation
Authors: Hideki Oki, Motoshi Abe, Junichi Miyao, Takio Kurita
Date: 2020-04-17 00:00:00
Tasks: Knowledge Distillation, Metric Learning, Triplet

In recent years, deep learning has spread rapidly, and deeper, larger models have been proposed. However, the calculation cost becomes enormous as the size of the models becomes larger. Various techniques for compressing the size of the models have been proposed to improve performance while reducing computational costs. One of the methods to compress the size of the models is knowledge distillation (KD). Knowledge distillation is a technique for transferring knowledge of deep or ensemble models with many parameters (teacher model) to smaller shallow models (student model). Since the purpose of knowledge distillation is to increase the similarity between the teacher model and the student model, we propose to introduce the concept of metric learning into knowledge distillation to make the student model closer to the teacher model using pairs or triplets of the training samples. In metric learning, the researchers are developing the methods to build a model that can increase the similarity of outputs for similar samples. Metric learning aims at reducing the distance between similar and increasing the distance between dissimilar. The functionality of the metric learning to reduce the differences between similar outputs can be used for the knowledge distillation to reduce the differences between the outputs of the teacher model and the student model. Since the outputs of the teacher model for different objects are usually different, the student model needs to distinguish them. We think that metric learning can clarify the difference between the different outputs, and the performance of the student model could be improved. We have performed experiments to compare the proposed method with state-of-the-art knowledge distillation methods.
Estimation of sparse Gaussian graphical models with hidden clustering structure
Authors: Meixia Lin, Defeng Sun, Kim-Chuan Toh, Chengjing Wang
Date: 2020-04-17 00:00:00
Tasks: Clustering

Estimation of Gaussian graphical models is important in natural science when modeling the statistical relationships between variables in the form of a graph. The sparsity and clustering structure of the concentration matrix is enforced to reduce model complexity and describe inherent regularities. We propose a model to estimate the sparse Gaussian graphical models with hidden clustering structure, which also allows additional linear constraints to be imposed on the concentration matrix. We design an efficient two-phase algorithm for solving the proposed model. We develop a symmetric Gauss-Seidel based alternating direction method of the multipliers (sGS-ADMM) to generate an initial point to warm-start the second phase algorithm, which is a proximal augmented Lagrangian method (pALM), to get a solution with high accuracy. Numerical experiments on both synthetic data and real data demonstrate the good performance of our model, as well as the efficiency and robustness of our proposed algorithm.
Show Us the Way: Learning to Manage Dialog from Demonstrations
Authors: Gabriel Gordon-Hall, Philip John Gorinski, Gerasimos Lampouras, Ignacio Iacobacci
Date: 2020-04-17 00:00:00
Tasks: dialog state tracking, Management, Natural Language Understanding, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Text Generation

We present our submission to the End-to-End Multi-Domain Dialog Challenge Track of the Eighth Dialog System Technology Challenge. Our proposed dialog system adopts a pipeline architecture, with distinct components for Natural Language Understanding, Dialog State Tracking, Dialog Management and Natural Language Generation. At the core of our system is a reinforcement learning algorithm which uses Deep Q-learning from Demonstrations to learn a dialog policy with the help of expert examples. We find that demonstrations are essential to training an accurate dialog policy where both state and action spaces are large. Evaluation of our Dialog Management component shows that our approach is effective - beating supervised and reinforcement learning baselines.
Incorporating Multiple Cluster Centers for Multi-Label Learning
Authors: Senlin Shu, Fengmao Lv, Yan Yan, Li Li, Shuo He, Jun He
Date: 2020-04-17 00:00:00
Tasks: Clustering, Data Augmentation, Multi-Label Learning

Multi-label learning deals with the problem that each instance is associated with multiple labels simultaneously. Most of the existing approaches aim to improve the performance of multi-label learning by exploiting label correlations. Although the data augmentation technique is widely used in many machine learning tasks, it is still unclear whether data augmentation is helpful to multi-label learning. In this article, we propose to leverage the data augmentation technique to improve the performance of multi-label learning. Specifically, we first propose a novel data augmentation approach that performs clustering on the real examples and treats the cluster centers as virtual examples, and these virtual examples naturally embody the local label correlations and label importances. Then, motivated by the cluster assumption that examples in the same cluster should have the same label, we propose a novel regularization term to bridge the gap between the real examples and virtual examples, which can promote the local smoothness of the learning function. Extensive experimental results on a number of real-world multi-label datasets clearly demonstrate that our proposed approach outperforms the state-of-the-art counterparts.
LiteDenseNet: A Lightweight Network for Hyperspectral Image Classification
Authors: Rui Li, Chenxi Duan
Date: 2020-04-17 00:00:00
Tasks: Classification, Deep Learning, General Classification, Hyperspectral Image Classification, image-classification, Image Classification

Hyperspectral Image (HSI) classification based on deep learning has been an attractive area in recent years. However, as a kind of data-driven algorithm, deep learning method usually requires numerous computational resources and high-quality labelled dataset, while the cost of high-performance computing and data annotation is expensive. In this paper, to reduce dependence on massive calculation and labelled samples, we propose a lightweight network architecture (LiteDenseNet) based on DenseNet for Hyperspectral Image Classification. Inspired by GoogLeNet and PeleeNet, we design a 3D two-way dense layer to capture the local and global features of the input. As convolution is a computationally intensive operation, we introduce group convolution to decrease calculation cost and parameter size further. Thus, the number of parameters and the consumptions of calculation are observably less than contrapositive deep learning methods, which means LiteDenseNet owns simpler architecture and higher efficiency. A series of quantitative experiences on 6 widely used hyperspectral datasets show that the proposed LiteDenseNet obtains the state-of-the-art performance, even though when the absence of labelled samples is severe.
Multi-Scale Supervised 3D U-Net for Kidneys and Kidney Tumor Segmentation
Authors: Wenshuai Zhao, Dihong Jiang, Jorge Peña Queralta, Tomi Westerlund
Date: 2020-04-17 00:00:00
Tasks: Computed Tomography (CT), Segmentation, Tumor Segmentation

Accurate segmentation of kidneys and kidney tumors is an essential step for radiomic analysis as well as developing advanced surgical planning techniques. In clinical analysis, the segmentation is currently performed by clinicians from the visual inspection images gathered through a computed tomography (CT) scan. This process is laborious and its success significantly depends on previous experience. Moreover, the uncertainty in the tumor location and heterogeneity of scans across patients increases the error rate. To tackle this issue, computer-aided segmentation based on deep learning techniques have become increasingly popular. We present a multi-scale supervised 3D U-Net, MSS U-Net, to automatically segment kidneys and kidney tumors from CT images. Our architecture combines deep supervision with exponential logarithmic loss to increase the 3D U-Net training efficiency. Furthermore, we introduce a connected-component based post processing method to enhance the performance of the overall process. This architecture shows superior performance compared to state-of-the-art works using data from KiTS19 public dataset, with the Dice coefficient of kidney and tumor up to 0.969 and 0.805 respectively. The segmentation techniques introduced in this paper have been tested in the KiTS19 challenge with its corresponding dataset.
Cascaded Context Enhancement Network for Automatic Skin Lesion Segmentation
Authors: Ruxin Wang, Shuyuan Chen, Chaojie Ji, Ye Li
Date: 2020-04-17 00:00:00
Tasks: Lesion Segmentation, Melanoma Diagnosis, Segmentation, Semantic Segmentation, Skin Lesion Segmentation

Skin lesion segmentation is an important step for automatic melanoma diagnosis. Due to the non-negligible diversity of lesions from different patients, extracting powerful context for fine-grained semantic segmentation is still challenging today. Although the deep convolutional neural network (CNNs) have made significant improvements on skin lesion segmentation, they often fail to reserve the spatial details and long-range dependencies context due to consecutive convolution striding and pooling operations inside CNNs. In this paper, we formulate a cascaded context enhancement neural network for automatic skin lesion segmentation. A new cascaded context aggregation (CCA) module with a gate-based information integration approach is proposed to sequentially and selectively aggregate original image and multi-level features from the encoder sub-network. The generated context is further utilized to guide discriminative features extraction by the designed context-guided local affinity (CGL) module. Furthermore, an auxiliary loss is added to the CCA module for refining the prediction. In our work, we evaluate our approach on four public skin dermoscopy image datasets. The proposed method achieves the Jaccard Index (JA) of 87.1%, 80.3%, 83.4%, and 86.6% on ISIC-2016, ISIC-2017, ISIC-2018, and PH2 datasets, which are higher than other state-of-the-art models respectively.
RPnet: A Deep Learning approach for robust R Peak detection in noisy ECG
Authors: Sricharan Vijayarangan, Vignesh R, Balamurali Murugesan, Preejith SP, Jayaraj Joseph, Mohansankar Sivaprakasam
Date: 2020-04-17 00:00:00
Tasks: Heart Rate Variability, Image-to-Image Translation

Automatic detection of R-peaks in an Electrocardiogram signal is crucial in a multitude of applications including Heart Rate Variability (HRV) analysis and Cardio Vascular Disease(CVD) diagnosis. Although there have been numerous approaches that have successfully addressed the problem, there has been a notable dip in the performance of these existing detectors on ECG episodes that contain noise and HRV Irregulates. On the other hand, Deep Learning(DL) based methods have shown to be adept at modelling data that contain noise. In image to image translation, Unet is the fundamental block in many of the networks. In this work, a novel application of the Unet combined with Inception and Residual blocks is proposed to perform the extraction of R-peaks from an ECG. Furthermore, the problem formulation also robustly deals with issues of variability and sparsity of ECG R-peaks. The proposed network was trained on a database containing ECG episodes that have CVD and was tested against three traditional ECG detectors on a validation set. The model achieved an F1 score of 0.9837, which is a substantial improvement over the other beat detectors. Furthermore, the model was also evaluated on three other databases. The proposed network achieved high F1 scores across all datasets which established its generalizing capacity. Additionally, a thorough analysis of the model's performance in the presence of different levels of noise was carried out.
A stochastic approach to handle knapsack problems in the creation of ensembles
Authors: Andras Hajdu, Gyorgy Terdik, Attila Tiba, Henrietta Toman
Date: 2020-04-17 00:00:00

Ensemble-based methods are highly popular approaches that increase the accuracy of a decision by aggregating the opinions of individual voters. The common point is to maximize accuracy; however, a natural limitation occurs if incremental costs are also assigned to the individual voters. Consequently, we investigate creating ensembles under an additional constraint on the total cost of the members. This task can be formulated as a knapsack problem, where the energy is the ensemble accuracy formed by some aggregation rules. However, the generally applied aggregation rules lead to a nonseparable energy function, which takes the common solution tools -- such as dynamic programming -- out of action. We introduce a novel stochastic approach that considers the energy as the joint probability function of the member accuracies. This type of knowledge can be efficiently incorporated in a stochastic search process as a stopping rule, since we have the information on the expected accuracy or, alternatively, the probability of finding more accurate ensembles. Experimental analyses of the created ensembles of pattern classifiers and object detectors confirm the efficiency of our approach. Moreover, we propose a novel stochastic search strategy that better fits the energy, compared with general approaches such as simulated annealing.
Recommendation system using a deep learning and graph analysis approach
Authors: Mahdi Kherad, Amir Jalaly Bidgoly
Date: 2020-04-17 00:00:00
Tasks: Community Detection, Deep Learning, Recommendation Systems

When a user connects to the Internet to fulfill his needs, he often encounters a huge amount of related information. Recommender systems are the techniques for massively filtering information and offering the items that users find them satisfying and interesting. The advances in machine learning methods, especially deep learning, have led to great achievements in recommender systems, although these systems still suffer from challenges such as cold-start and sparsity problems. To solve these problems, context information such as user communication network is usually used. In this paper, we have proposed a novel recommendation method based on Matrix Factorization and graph analysis methods. In addition, we leverage deep Autoencoders to initialize users and items latent factors, and deep embedding method gathers users' latent factors from the user trust graph. The proposed method is implemented on two standard datasets. The experimental results and comparisons demonstrate that the proposed approach is superior to the existing state-of-the-art recommendation methods. Our approach outperforms other comparative methods and achieves great improvements.
Fast and Accurate Deep Bidirectional Language Representations for Unsupervised Learning
Authors: Joongbo Shin, Yoonhyung Lee, Seunghyun Yoon, Kyomin Jung
Date: 2020-04-17 00:00:00
Proceeding: ACL 2020 6
Tasks: CPU, Language Modeling, Language Modelling, Reranking, Semantic Similarity, Semantic Textual Similarity

Even though BERT achieves successful performance improvements in various supervised learning tasks, applying BERT for unsupervised tasks still holds a limitation that it requires repetitive inference for computing contextual language representations. To resolve the limitation, we propose a novel deep bidirectional language model called Transformer-based Text Autoencoder (T-TA). The T-TA computes contextual language representations without repetition and has benefits of the deep bidirectional architecture like BERT. In run-time experiments on CPU environments, the proposed T-TA performs over six times faster than the BERT-based model in the reranking task and twelve times faster in the semantic similarity task. Furthermore, the T-TA shows competitive or even better accuracies than those of BERT on the above tasks.
Fast Soft Color Segmentation
Authors: Naofumi Akimoto, Huachun Zhu, Yanghua Jin, Yoshimitsu Aoki
Date: 2020-04-17 00:00:00
Proceeding: CVPR 2020 6
Tasks: Segmentation, Video Editing

We address the problem of soft color segmentation, defined as decomposing a given image into several RGBA layers, each containing only homogeneous color regions. The resulting layers from decomposition pave the way for applications that benefit from layer-based editing, such as recoloring and compositing of images and videos. The current state-of-the-art approach for this problem is hindered by slow processing time due to its iterative nature, and consequently does not scale to certain real-world scenarios. To address this issue, we propose a neural network based method for this task that decomposes a given image into multiple layers in a single forward pass. Furthermore, our method separately decomposes the color layers and the alpha channel layers. By leveraging a novel training objective, our method achieves proper assignment of colors amongst layers. As a consequence, our method achieve promising quality without existing issue of inference speed for iterative approaches. Our thorough experimental analysis shows that our method produces qualitative and quantitative results comparable to previous methods while achieving a 300,000x speed improvement. Finally, we utilize our proposed method on several applications, and demonstrate its speed advantage, especially in video editing.
Statistical Learning Guarantees for Compressive Clustering and Compressive Mixture Modeling
Authors: Rémi Gribonval, Gilles Blanchard, Nicolas Keriven, Yann Traonmilin
Date: 2020-04-17 00:00:00
Tasks: Clustering

We provide statistical learning guarantees for two unsupervised learning tasks in the context of compressive statistical learning, a general framework for resource-efficient large-scale learning that we introduced in a companion paper.The principle of compressive statistical learning is to compress a training collection, in one pass, into a low-dimensional sketch (a vector of random empirical generalized moments) that captures the information relevant to the considered learning task. We explicitly describe and analyze random feature functions which empirical averages preserve the needed information for compressive clustering and compressive Gaussian mixture modeling with fixed known variance, and establish sufficient sketch sizes given the problem dimensions.
Meta-Meta Classification for One-Shot Learning
Authors: Arkabandhu Chowdhury, Dipak Chaudhari, Swarat Chaudhuri, Chris Jermaine
Date: 2020-04-17 00:00:00
Tasks: Classification, Few-Shot Learning, General Classification, Meta-Learning, One-Shot Learning, Small Data Image Classification

We present a new approach, called meta-meta classification, to learning in small-data settings. In this approach, one uses a large set of learning problems to design an ensemble of learners, where each learner has high bias and low variance and is skilled at solving a specific type of learning problem. The meta-meta classifier learns how to examine a given learning problem and combine the various learners to solve the problem. The meta-meta learning approach is especially suited to solving few-shot learning tasks, as it is easier to learn to classify a new learning problem with little data than it is to apply a learning algorithm to a small data set. We evaluate the approach on a one-shot, one-class-versus-all classification task and show that it is able to outperform traditional meta-learning as well as ensembling approaches.
Image Processing Based Scene-Text Detection and Recognition with Tesseract
Authors: Ebin Zacharias, Martin Teuchler, Bénédicte Bernier
Date: 2020-04-17 00:00:00
Tasks: Optical Character Recognition, Optical Character Recognition (OCR), Scene Text Detection, Text Detection

Text Recognition is one of the challenging tasks of computer vision with considerable practical interest. Optical character recognition (OCR) enables different applications for automation. This project focuses on word detection and recognition in natural images. In comparison to reading text in scanned documents, the targeted problem is significantly more challenging. The use case in focus facilitates the possibility to detect the text area in natural scenes with greater accuracy because of the availability of images under constraints. This is achieved using a camera mounted on a truck capturing likewise images round-the-clock. The detected text area is then recognized using Tesseract OCR engine. Even though it benefits low computational power requirements, the model is limited to only specific use cases. This paper discusses a critical false positive case scenario occurred while testing and elaborates the strategy used to alleviate the problem. The project achieved a correct character recognition rate of more than 80\%. This paper outlines the stages of development, the major challenges and some of the interesting findings of the project.
Neural Approaches for Data Driven Dependency Parsing in Sanskrit
Authors: Amrith Krishna, Ashim Gupta, Deepak Garasangi, Jivnesh Sandhan, Pavankumar Satuluri, Pawan Goyal
Date: 2020-04-17 00:00:00
Tasks: Dependency Parsing

Data-driven approaches for dependency parsing have been of great interest in Natural Language Processing for the past couple of decades. However, Sanskrit still lacks a robust purely data-driven dependency parser, probably with an exception to Krishna (2019). This can primarily be attributed to the lack of availability of task-specific labelled data and the morphologically rich nature of the language. In this work, we evaluate four different data-driven machine learning models, originally proposed for different languages, and compare their performances on Sanskrit data. We experiment with 2 graph based and 2 transition based parsers. We compare the performance of each of the models in a low-resource setting, with 1,500 sentences for training. Further, since our focus is on the learning power of each of the models, we do not incorporate any Sanskrit specific features explicitly into the models, and rather use the default settings in each of the paper for obtaining the feature functions. In this work, we analyse the performance of the parsers using both an in-domain and an out-of-domain test dataset. We also investigate the impact of word ordering in which the sentences are provided as input to these systems, by parsing verses and their corresponding prose order (anvaya) sentences.