pwc-archive/papers-with-abstracts huggingface dataset:
Self-supervised Fine-tuning for Correcting Super-Resolution Convolutional Neural Networks
Authors: Alice Lucas, Santiago Lopez-Tapia, Rafael Molina, Aggelos K. Katsaggelos
Date: 2019-12-30 00:00:00
Tasks: Image Enhancement, Super-Resolution, Video Super-Resolution

While Convolutional Neural Networks (CNNs) trained for image and video super-resolution (SR) regularly achieve new state-of-the-art performance, they also suffer from significant drawbacks. One of their limitations is their lack of robustness to unseen image formation models during training. Other limitations include the generation of artifacts and hallucinated content when training Generative Adversarial Networks (GANs) for SR. While the Deep Learning literature focuses on presenting new training schemes and settings to resolve these various issues, we show that one can avoid training and correct for SR results with a fully self-supervised fine-tuning approach. More specifically, at test time, given an image and its known image formation model, we fine-tune the parameters of the trained network and iteratively update them using a data fidelity loss. We apply our fine-tuning algorithm on multiple image and video SR CNNs and show that it can successfully correct for a sub-optimal SR solution by entirely relying on internal learning at test time. We apply our method on the problem of fine-tuning for unseen image formation models and on removal of artifacts introduced by GANs.
Video Depth Estimation by Fusing Flow-to-Depth Proposals
Authors: Jiaxin Xie, Chenyang Lei, Zhuwen Li, Li Erran Li, Qifeng Chen
Date: 2019-12-30 00:00:00
Tasks: Depth Estimation, Optical Flow Estimation

Depth from a monocular video can enable billions of devices and robots with a single camera to see the world in 3D. In this paper, we present an approach with a differentiable flow-to-depth layer for video depth estimation. The model consists of a flow-to-depth layer, a camera pose refinement module, and a depth fusion network. Given optical flow and camera pose, our flow-to-depth layer generates depth proposals and the corresponding confidence maps by explicitly solving an epipolar geometry optimization problem. Our flow-to-depth layer is differentiable, and thus we can refine camera poses by maximizing the aggregated confidence in the camera pose refinement module. Our depth fusion network can utilize depth proposals and their confidence maps inferred from different adjacent frames to produce the final depth map. Furthermore, the depth fusion network can additionally take the depth proposals generated by other methods to improve the results further. The experiments on three public datasets show that our approach outperforms state-of-the-art depth estimation methods, and has reasonable cross dataset generalization capability: our model trained on KITTI still performs well on the unseen Waymo dataset.
Adaptive Discrete Smoothing for High-Dimensional and Nonlinear Panel Data
Authors: Xi Chen, Ye Luo, Martin Spindler
Date: 2019-12-30 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, Vocal Bursts Intensity Prediction

In this paper we develop a data-driven smoothing technique for high-dimensional and non-linear panel data models. We allow for individual specific (non-linear) functions and estimation with econometric or machine learning methods by using weighted observations from other individuals. The weights are determined by a data-driven way and depend on the similarity between the corresponding functions and are measured based on initial estimates. The key feature of such a procedure is that it clusters individuals based on the distance / similarity between them, estimated in a first stage. Our estimation method can be combined with various statistical estimation procedures, in particular modern machine learning methods which are in particular fruitful in the high-dimensional case and with complex, heterogeneous data. The approach can be interpreted as a \textquotedblleft soft-clustering\textquotedblright\ in comparison to traditional\textquotedblleft\ hard clustering\textquotedblright that assigns each individual to exactly one group. We conduct a simulation study which shows that the prediction can be greatly improved by using our estimator. Finally, we analyze a big data set from didichuxing.com, a leading company in transportation industry, to analyze and predict the gap between supply and demand based on a large set of covariates. Our estimator clearly performs much better in out-of-sample prediction compared to existing linear panel data estimators.
Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium
Authors: Bart Cockx, Michael Lechner, Joost Bollens
Date: 2019-12-30 00:00:00
Tasks: BIG-bench Machine Learning

Based on administrative data of unemployed in Belgium, we estimate the labour market effects of three training programmes at various aggregation levels using Modified Causal Forests, a causal machine learning estimator. While all programmes have positive effects after the lock-in period, we find substantial heterogeneity across programmes and unemployed. Simulations show that 'black-box' rules that reassign unemployed to programmes that maximise estimated individual gains can considerably improve effectiveness: up to 20 percent more (less) time spent in (un)employment within a 30 months window. A shallow policy tree delivers a simple rule that realizes about 70 percent of this gain.
Searching for Stage-wise Neural Graphs In the Limit
Authors: Xin Zhou, Dejing Dou, Boyang Li
Date: 2019-12-30 00:00:00
Tasks: Neural Architecture Search

Search space is a key consideration for neural architecture search. Recently, Xie et al. (2019) found that randomly generated networks from the same distribution perform similarly, which suggests we should search for random graph distributions instead of graphs. We propose graphon as a new search space. A graphon is the limit of Cauchy sequence of graphs and a scale-free probabilistic distribution, from which graphs of different number of nodes can be drawn. By utilizing properties of the graphon space and the associated cut-distance metric, we develop theoretically motivated techniques that search for and scale up small-capacity stage-wise graphs found on small datasets to large-capacity graphs that can handle ImageNet. The scaled stage-wise graphs outperform DenseNet and randomly wired Watts-Strogatz networks, indicating the benefits of graphon theory in NAS applications.
Defending from adversarial examples with a two-stream architecture
Authors: Hao Ge, Xiaoguang Tu, Mei Xie, Zheng Ma
Date: 2019-12-30 00:00:00
Tasks: Vocal Bursts Valence Prediction

In recent years, deep learning has shown impressive performance on many tasks. However, recent researches showed that deep learning systems are vulnerable to small, specially crafted perturbations that are imperceptible to humans. Images with such perturbations are the so called adversarial examples, which have proven to be an indisputable threat to the DNN based applications. The lack of better understanding of the DNNs has prevented the development of efficient defenses against adversarial examples. In this paper, we propose a two-stream architecture to protect CNN from attacking by adversarial examples. Our model draws on the idea of "two-stream" which commonly used in the security field, and successfully defends different kinds of attack methods by the differences of "high-resolution" and "low-resolution" networks in feature extraction. We provide a reasonable interpretation on why our two-stream architecture is difficult to defeat, and show experimentally that our method is hard to defeat with state-of-the-art attacks. We demonstrate that our two-stream architecture is robust to adversarial examples built by currently known attacking algorithms.
Pareto Multi-Task Learning
Authors: Xi Lin, Hui-Ling Zhen, Zhenhua Li, Qingfu Zhang, Sam Kwong
Date: 2019-12-30 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Multiobjective Optimization, Multi-Task Learning

Multi-task learning is a powerful method for solving multiple correlated tasks simultaneously. However, it is often impossible to find one single solution to optimize all the tasks, since different tasks might conflict with each other. Recently, a novel method is proposed to find one single Pareto optimal solution with good trade-off among different tasks by casting multi-task learning as multiobjective optimization. In this paper, we generalize this idea and propose a novel Pareto multi-task learning algorithm (Pareto MTL) to find a set of well-distributed Pareto solutions which can represent different trade-offs among different tasks. The proposed algorithm first formulates a multi-task learning problem as a multiobjective optimization problem, and then decomposes the multiobjective optimization problem into a set of constrained subproblems with different trade-off preferences. By solving these subproblems in parallel, Pareto MTL can find a set of well-representative Pareto optimal solutions with different trade-off among all tasks. Practitioners can easily select their preferred solution from these Pareto solutions, or use different trade-off solutions for different situations. Experimental results confirm that the proposed algorithm can generate well-representative solutions and outperform some state-of-the-art algorithms on many multi-task learning applications.
Generative Memorize-Then-Recall framework for low bit-rate Surveillance Video Compression
Authors: Yaojun Wu, Tianyu He, Zhibo Chen
Date: 2019-12-30 00:00:00
Tasks: Generative Adversarial Network, Motion Compensation, Video Compression

Applications of surveillance video have developed rapidly in recent years to protect public safety and daily life, which often detect and recognize objects in video sequences. Traditional coding frameworks remove temporal redundancy in surveillance video by block-wise motion compensation, lacking the extraction and utilization of inherent structure information. In this paper, we figure out this issue by disentangling surveillance video into the structure of a global spatio-temporal feature (memory) for Group of Picture (GoP) and skeleton for each frame (clue). The memory is obtained by sequentially feeding frame inside GoP into a recurrent neural network, describing appearance for objects that appeared inside GoP. While the skeleton is calculated by a pose estimator, it is regarded as a clue to recall memory. Furthermore, an attention mechanism is introduced to obtain the relation between appearance and skeletons. Finally, we employ generative adversarial network to reconstruct each frame. Experimental results indicate that our method effectively generates realistic reconstruction based on appearance and skeleton, which show much higher compression performance on surveillance video compared with the latest video compression standard H.265.
Variance Reduced Local SGD with Lower Communication Complexity
Authors: Xianfeng Liang, Shuheng Shen, Jingchang Liu, Zhen Pan, Enhong Chen, Yifei Cheng
Date: 2019-12-30 00:00:00
Tasks: BIG-bench Machine Learning

To accelerate the training of machine learning models, distributed stochastic gradient descent (SGD) and its variants have been widely adopted, which apply multiple workers in parallel to speed up training. Among them, Local SGD has gained much attention due to its lower communication cost. Nevertheless, when the data distribution on workers is non-identical, Local SGD requires $O(T^{\frac{3}{4}} N^{\frac{3}{4}})$ communications to maintain its \emph{linear iteration speedup} property, where $T$ is the total number of iterations and $N$ is the number of workers. In this paper, we propose Variance Reduced Local SGD (VRL-SGD) to further reduce the communication complexity. Benefiting from eliminating the dependency on the gradient variance among workers, we theoretically prove that VRL-SGD achieves a \emph{linear iteration speedup} with a lower communication complexity $O(T^{\frac{1}{2}} N^{\frac{3}{2}})$ even if workers access non-identical datasets. We conduct experiments on three machine learning tasks, and the experimental results demonstrate that VRL-SGD performs impressively better than Local SGD when the data among workers are quite diverse.
Causal-Anticausal Decomposition of Speech using Complex Cepstrum for Glottal Source Estimation
Authors: Thomas Drugman, Baris Bozkurt, Thierry Dutoit
Date: 2019-12-30 00:00:00

Complex cepstrum is known in the literature for linearly separating causal and anticausal components. Relying on advances achieved by the Zeros of the Z-Transform (ZZT) technique, we here investigate the possibility of using complex cepstrum for glottal flow estimation on a large-scale database. Via a systematic study of the windowing effects on the deconvolution quality, we show that the complex cepstrum causal-anticausal decomposition can be effectively used for glottal flow estimation when specific windowing criteria are met. It is also shown that this complex cepstral decomposition gives similar glottal estimates as obtained with the ZZT method. However, as complex cepstrum uses FFT operations instead of requiring the factoring of high-degree polynomials, the method benefits from a much higher speed. Finally in our tests on a large corpus of real expressive speech, we show that the proposed method has the potential to be used for voice quality analysis.
Multi-modality super-resolution loss for GAN-based super-resolution of clinical CT images using micro CT image database
Authors: Tong Zheng, Hirohisa ODA, Takayasu MORIYA, Shota NAKAMURA, Masahiro Oda, Masaki MORI, Horitsugu Takabatake, Hiroshi NATORI, Kensaku MORI
Date: 2019-12-30 00:00:00
Tasks: Computed Tomography (CT), Super-Resolution, Translation

This paper newly introduces multi-modality loss function for GAN-based super-resolution that can maintain image structure and intensity on unpaired training dataset of clinical CT and micro CT volumes. Precise non-invasive diagnosis of lung cancer mainly utilizes 3D multidetector computed-tomography (CT) data. On the other hand, we can take micro CT images of resected lung specimen in 50 micro meter or higher resolution. However, micro CT scanning cannot be applied to living human imaging. For obtaining highly detailed information such as cancer invasion area from pre-operative clinical CT volumes of lung cancer patients, super-resolution (SR) of clinical CT volumes to $\mu$CT level might be one of substitutive solutions. While most SR methods require paired low- and high-resolution images for training, it is infeasible to obtain precisely paired clinical CT and micro CT volumes. We aim to propose unpaired SR approaches for clincial CT using micro CT images based on unpaired image translation methods such as CycleGAN or UNIT. Since clinical CT and micro CT are very different in structure and intensity, direct application of GAN-based unpaired image translation methods in super-resolution tends to generate arbitrary images. Aiming to solve this problem, we propose new loss function called multi-modality loss function to maintain the similarity of input images and corresponding output images in super-resolution task. Experimental results demonstrated that the newly proposed loss function made CycleGAN and UNIT to successfully perform SR of clinical CT images of lung cancer patients into micro CT level resolution, while original CycleGAN and UNIT failed in super-resolution.
Randomly Projected Additive Gaussian Processes for Regression
Authors: Ian A. Delbridge, David S. Bindel, Andrew Gordon Wilson
Date: 2019-12-30 00:00:00
Proceeding: ICML 2020 1
Tasks: Gaussian Processes, regression, Small Data Image Classification

Gaussian processes (GPs) provide flexible distributions over functions, with inductive biases controlled by a kernel. However, in many applications Gaussian processes can struggle with even moderate input dimensionality. Learning a low dimensional projection can help alleviate this curse of dimensionality, but introduces many trainable hyperparameters, which can be cumbersome, especially in the small data regime. We use additive sums of kernels for GP regression, where each kernel operates on a different random projection of its inputs. Surprisingly, we find that as the number of random projections increases, the predictive performance of this approach quickly converges to the performance of a kernel operating on the original full dimensional inputs, over a wide range of data sets, even if we are projecting into a single dimension. As a consequence, many problems can remarkably be reduced to one dimensional input spaces, without learning a transformation. We prove this convergence and its rate, and additionally propose a deterministic approach that converges more quickly than purely random projections. Moreover, we demonstrate our approach can achieve faster inference and improved predictive accuracy for high-dimensional inputs compared to kernels in the original input space.
Early Detection of Diabetic Retinopathy and Severity Scale Measurement: A Progressive Review & Scopes
Authors: Asma Khatun, Sk. Golam Sarowar Hossain
Date: 2019-12-30 00:00:00
Tasks: GPU, Survey

Early detection of diabetic retinopathy prevents visual loss and blindness of a human eye. Based on the types of feature extraction method used, DR detection method can be broadly classified as Deep Convolutional Neural Network (CNN) based and traditional feature extraction (machine learning) based. This paper presents a comprehensive survey of existing feature extraction methods based on Deep CNN and conventional feature extraction for DR detection. In addition to that, this paper focuses on the severity scale measurement of the DR detection and to the best of our knowledge this is the first survey paper which covers severity grading scale. It is also necessary to mention that this is the first study which reviews the proposed Deep CNN based method in the state of the art for DR detection methods. This study discovers that recently proposed deep learning based DR detection methods provides higher accuracy than existing traditional feature extraction methods in the literature and also useful in large scale datasets. However, deep learning based methods require GPU implementation to get the desirable output. The one of the other major finding of this paper is that there are no obvious standard severity scale detection criteria to measure the grading. Some used binary class while many other used multi stage class.
ICSTrace: A Malicious IP Traceback Model for Attacking Data of Industrial Control System
Authors: Feng Xiao, Qiang Xu
Date: 2019-12-30 00:00:00
Tasks: Clustering

Considering the attacks against industrial control system are mostly organized and premeditated actions, IP traceback is significant for the security of industrial control system. Based on the infrastructure of the Internet, we have developed a novel malicious IP traceback model-ICSTrace, without deploying any new services. The model extracts the function codes and their parameters from the attack data according to the format of industrial control protocol, and employs a short sequence probability method to transform the function codes and their parameter into a vector, which characterizes the attack pattern of malicious IP addresses. Furthermore, a Partial Seeded K-Means algorithm is proposed for the pattern's clustering, which helps in tracing the attacks back to an organization. ICSTrace is evaluated basing on the attack data captured by the large-scale deployed honeypots for industrial control system, and the results demonstrate that ICSTrace is effective on malicious IP traceback in industrial control system.
Neural Architecture Search on Acoustic Scene Classification
Authors: Jixiang Li, Chuming Liang, Bo Zhang, Zhao Wang, Fei Xiang, Xiangxiang Chu
Date: 2019-12-30 00:00:00
Tasks: Acoustic Scene Classification, Classification, General Classification, Neural Architecture Search, Scene Classification

Convolutional neural networks are widely adopted in Acoustic Scene Classification (ASC) tasks, but they generally carry a heavy computational burden. In this work, we propose a lightweight yet high-performing baseline network inspired by MobileNetV2, which replaces square convolutional kernels with unidirectional ones to extract features alternately in temporal and frequency dimensions. Furthermore, we explore a dynamic architecture space built on the basis of the proposed baseline with the recent Neural Architecture Search (NAS) paradigm, which first trains a supernet that incorporates all candidate networks and then applies a well-known evolutionary algorithm NSGA-II to discover more efficient networks with higher accuracy and lower computational cost. Experimental results demonstrate that our searched network is competent in ASC tasks, which achieves 90.3% F1-score on the DCASE2018 task 5 evaluation set, marking a new state-of-the-art performance while saving 25% of FLOPs compared to our baseline network.
Disentangled Representation Learning with Wasserstein Total Correlation
Authors: Yijun Xiao, William Yang Wang
Date: 2019-12-30 00:00:00
Tasks: Disentanglement, Representation Learning

Unsupervised learning of disentangled representations involves uncovering of different factors of variations that contribute to the data generation process. Total correlation penalization has been a key component in recent methods towards disentanglement. However, Kullback-Leibler (KL) divergence-based total correlation is metric-agnostic and sensitive to data samples. In this paper, we introduce Wasserstein total correlation in both variational autoencoder and Wasserstein autoencoder settings to learn disentangled latent representations. A critic is adversarially trained along with the main objective to estimate the Wasserstein total correlation term. We discuss the benefits of using Wasserstein distance over KL divergence to measure independence and conduct quantitative and qualitative experiments on several data sets. Moreover, we introduce a new metric to measure disentanglement. We show that the proposed approach has comparable performances on disentanglement with smaller sacrifices in reconstruction abilities.
An End-to-End Joint Learning Scheme of Image Compression and Quality Enhancement with Improved Entropy Minimization
Authors: Jooyoung Lee, Seunghyun Cho, Munchurl Kim
Date: 2019-12-30 00:00:00
Tasks: Image Compression, MS-SSIM, SSIM

Recently, learned image compression methods have been actively studied. Among them, entropy-minimization based approaches have achieved superior results compared to conventional image codecs such as BPG and JPEG2000. However, the quality enhancement and rate-minimization are conflictively coupled in the process of image compression. That is, maintaining high image quality entails less compression and vice versa. However, by jointly training separate quality enhancement in conjunction with image compression, the coding efficiency can be improved. In this paper, we propose a novel joint learning scheme of image compression and quality enhancement, called JointIQ-Net, as well as entropy model improvement, thus achieving significantly improved coding efficiency against the previous methods. Our proposed JointIQ-Net combines an image compression sub-network and a quality enhancement sub-network in a cascade, both of which are end-to-end trained in a combined manner within the JointIQ-Net. Also the JointIQ-Net benefits from improved entropy-minimization that newly adopts a Gussian Mixture Model (GMM) and further exploits global context to estimate the probabilities of latent representations. In order to show the effectiveness of our proposed JointIQ-Net, extensive experiments have been performed, and showed that the JointIQ-Net achieves a remarkable performance improvement in coding efficiency in terms of both PSNR and MS-SSIM, compared to the previous learned image compression methods and the conventional codecs such as VVC Intra (VTM 7.1), BPG, and JPEG2000. To the best of our knowledge, this is the first end-to-end optimized image compression method that outperforms VTM 7.1 (Intra), the latest reference software of the VVC standard, in terms of the PSNR and MS-SSIM.