pwc-archive/papers-with-abstracts huggingface dataset:
Ethical Adversaries: Towards Mitigating Unfairness with Adversarial Machine Learning
Authors: Pieter Delobelle, Paul Temple, Gilles Perrouin, Benoît Frénay, Patrick Heymans, Bettina Berendt
Date: 2020-05-14 00:00:00
Tasks: BIG-bench Machine Learning, Fairness

Machine learning is being integrated into a growing number of critical systems with far-reaching impacts on society. Unexpected behaviour and unfair decision processes are coming under increasing scrutiny due to this widespread use and its theoretical considerations. Individuals, as well as organisations, notice, test, and criticize unfair results to hold model designers and deployers accountable. We offer a framework that assists these groups in mitigating unfair representations stemming from the training datasets. Our framework relies on two inter-operating adversaries to improve fairness. First, a model is trained with the goal of preventing the guessing of protected attributes' values while limiting utility losses. This first step optimizes the model's parameters for fairness. Second, the framework leverages evasion attacks from adversarial machine learning to generate new examples that will be misclassified. These new examples are then used to retrain and improve the model in the first step. These two steps are iteratively applied until a significant improvement in fairness is obtained. We evaluated our framework on well-studied datasets in the fairness literature -- including COMPAS -- where it can surpass other approaches concerning demographic parity, equality of opportunity and also the model's utility. We also illustrate our findings on the subtle difficulties when mitigating unfairness and highlight how our framework can assist model designers.
Industrial Federated Learning -- Requirements and System Design
Authors: Thomas Hiessl, Daniel Schall, Jana Kemnitz, Stefan Schulte
Date: 2020-05-14 00:00:00
Tasks: Federated Learning, Transfer Learning

Federated Learning (FL) is a very promising approach for improving decentralized Machine Learning (ML) models by exchanging knowledge between participating clients without revealing private data. Nevertheless, FL is still not tailored to the industrial context as strong data similarity is assumed for all FL tasks. This is rarely the case in industrial machine data with variations in machine type, operational- and environmental conditions. Therefore, we introduce an Industrial Federated Learning (IFL) system supporting knowledge exchange in continuously evaluated and updated FL cohorts of learning tasks with sufficient data similarity. This enables optimal collaboration of business partners in common ML problems, prevents negative knowledge transfer, and ensures resource optimization of involved edge devices.
RegQCNET: Deep Quality Control for Image-to-template Brain MRI Affine Registration
Authors: Baudouin Denis de Senneville, José V. Manjon, Pierrick Coupé
Date: 2020-05-14 00:00:00
Tasks: Brain Segmentation

Affine registration of one or several brain image(s) onto a common reference space is a necessary prerequisite for many image processing tasks, such as brain segmentation or functional analysis. Manual assessment of registration quality is a tedious and time-consuming task, especially in studies comprising a large amount of data. An automated and reliable quality control (QC) becomes mandatory. Moreover, the computation time of the QC must be also compatible with the processing of massive datasets. Therefore, an automated deep neural network approaches appear as a method of choice to automatically assess registration quality. In the current study, a compact 3D convolutional neural network (CNN), referred to as RegQCNET, is introduced to quantitatively predict the amplitude of an affine registration mismatch between a registered image and a reference template. This quantitative estimation of registration error is expressed using metric unit system. Therefore, a meaningful task-specific threshold can be manually or automatically defined in order to distinguish usable and non-usable images. The robustness of the proposed RegQCNET is first analyzed on lifespan brain images undergoing various simulated spatial transformations and intensity variations between training and testing. Secondly, the potential of RegQCNET to classify images as usable or non-usable is evaluated using both manual and automatic thresholds. During our experiments, automatic thresholds are estimated using several computer-assisted classification models through cross-validation. To this end we used expert's visual quality control estimated on a lifespan cohort of 3953 brains. Finally, the RegQCNET accuracy is compared to usual image features. Results show that the proposed deep learning QC is robust, fast and accurate to estimate affine registration error in processing pipeline.
A multicenter study on radiomic features from T$_2$-weighted images of a customized MR pelvic phantom setting the basis for robust radiomic models in clinics
Authors: Linda Bianchini, Joao Santinha, Nuno Loução, Mario Figueiredo, Francesca Botta, Daniela Origgi, Marta Cremonesi, Enrico Cassano, Nikolaos Papanikolaou, Alessandro Lascialfari
Date: 2020-05-14 00:00:00

In this study we investigated the repeatability and reproducibility of radiomic features extracted from MRI images and provide a workflow to identify robust features. 2D and 3D T$_2$-weighted images of a pelvic phantom were acquired on three scanners of two manufacturers and two magnetic field strengths. The repeatability and reproducibility of the radiomic features were assessed respectively by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC), considering repeated acquisitions with or without phantom repositioning, and with different scanner/acquisition type, and acquisition parameters. The features showing ICC/CCC > 0.9 were selected, and their dependence on shape information (Spearman's $\rho$> 0.8) was analyzed. They were classified for their ability to distinguish textures, after shuffling voxel intensities. From 944 2D features, 79.9% to 96.4% showed excellent repeatability in fixed position across all scanners. Much lower range (11.2% to 85.4%) was obtained after phantom repositioning. 3D extraction did not improve repeatability performance. Excellent reproducibility between scanners was observed in 4.6% to 15.6% of the features, at fixed imaging parameters. 82.4% to 94.9% of features showed excellent agreement when extracted from images acquired with TEs 5 ms apart (values decreased when increasing TE intervals) and 90.7% of the features exhibited excellent reproducibility for changes in TR. 2.0% of non-shape features were identified as providing only shape information. This study demonstrates that radiomic features are affected by specific MRI protocols. The use of our radiomic pelvic phantom allowed to identify unreliable features for radiomic analysis on T$_2$-weighted images. This paper proposes a general workflow to identify repeatable, reproducible, and informative radiomic features, fundamental to ensure robustness of clinical studies.
Detection and Retrieval of Out-of-Distribution Objects in Semantic Segmentation
Authors: Philipp Oberdiek, Matthias Rottmann, Gernot A. Fink
Date: 2020-05-14 00:00:00
Tasks: Dimensionality Reduction, Image Retrieval, Retrieval, Self-Driving Cars, Semantic Segmentation

When deploying deep learning technology in self-driving cars, deep neural networks are constantly exposed to domain shifts. These include, e.g., changes in weather conditions, time of day, and long-term temporal shift. In this work we utilize a deep neural network trained on the Cityscapes dataset containing urban street scenes and infer images from a different dataset, the A2D2 dataset, containing also countryside and highway images. We present a novel pipeline for semantic segmenation that detects out-of-distribution (OOD) segments by means of the deep neural network's prediction and performs image retrieval after feature extraction and dimensionality reduction on image patches. In our experiments we demonstrate that the deployed OOD approach is suitable for detecting out-of-distribution concepts. Furthermore, we evaluate the image patch retrieval qualitatively as well as quantitatively by means of the semi-compatible A2D2 ground truth and obtain mAP values of up to 52.2%.
Finet: Using Fine-grained Batch Normalization to Train Light-weight Neural Networks
Authors: Chunjie Luo, Jianfeng Zhan, Lei Wang, Wanling Gao
Date: 2020-05-14 00:00:00

To build light-weight network, we propose a new normalization, Fine-grained Batch Normalization (FBN). Different from Batch Normalization (BN), which normalizes the final summation of the weighted inputs, FBN normalizes the intermediate state of the summation. We propose a novel light-weight network based on FBN, called Finet. At training time, the convolutional layer with FBN can be seen as an inverted bottleneck mechanism. FBN can be fused into convolution at inference time. After fusion, Finet uses the standard convolution with equal channel width, thus makes the inference more efficient. On ImageNet classification dataset, Finet achieves the state-of-art performance (65.706% accuracy with 43M FLOPs, and 73.786% accuracy with 303M FLOPs), Moreover, experiments show that Finet is more efficient than other state-of-art light-weight networks.
A Semi-Supervised Assessor of Neural Architectures
Authors: Yehui Tang, Yunhe Wang, Yixing Xu, Hanting Chen, Chunjing Xu, Boxin Shi, Chao Xu, Qi Tian, Chang Xu
Date: 2020-05-14 00:00:00
Proceeding: CVPR 2020 6
Tasks: Neural Architecture Search

Neural architecture search (NAS) aims to automatically design deep neural networks of satisfactory performance. Wherein, architecture performance predictor is critical to efficiently value an intermediate neural architecture. But for the training of this predictor, a number of neural architectures and their corresponding real performance often have to be collected. In contrast with classical performance predictor optimized in a fully supervised way, this paper suggests a semi-supervised assessor of neural architectures. We employ an auto-encoder to discover meaningful representations of neural architectures. Taking each neural architecture as an individual instance in the search space, we construct a graph to capture their intrinsic similarities, where both labeled and unlabeled architectures are involved. A graph convolutional neural network is introduced to predict the performance of architectures based on the learned representations and their relation modeled by the graph. Extensive experimental results on the NAS-Benchmark-101 dataset demonstrated that our method is able to make a significant reduction on the required fully trained architectures for finding efficient architectures.
TAM: Temporal Adaptive Module for Video Recognition
Authors: Zhao-Yang Liu, Li-Min Wang, Wayne Wu, Chen Qian, Tong Lu
Date: 2020-05-14 00:00:00
Proceeding: ICCV 2021 10
Tasks: Action Recognition, Video Recognition

Video data is with complex temporal dynamics due to various factors such as camera motion, speed variation, and different activities. To effectively capture this diverse motion pattern, this paper presents a new temporal adaptive module ({\bf TAM}) to generate video-specific temporal kernels based on its own feature map. TAM proposes a unique two-level adaptive modeling scheme by decoupling the dynamic kernel into a location sensitive importance map and a location invariant aggregation weight. The importance map is learned in a local temporal window to capture short-term information, while the aggregation weight is generated from a global view with a focus on long-term structure. TAM is a modular block and could be integrated into 2D CNNs to yield a powerful video architecture (TANet) with a very small extra computational cost. The extensive experiments on Kinetics-400 and Something-Something datasets demonstrate that our TAM outperforms other temporal modeling methods consistently, and achieves the state-of-the-art performance under the similar complexity. The code is available at \url{ https://github.com/liu-zhy/temporal-adaptive-module}.
Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning
Authors: Kimin Lee, Younggyo Seo, Seung-Hyun Lee, Honglak Lee, Jinwoo Shin
Date: 2020-05-14 00:00:00
Proceeding: ICML 2020 1
Tasks: model, Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

Model-based reinforcement learning (RL) enjoys several benefits, such as data-efficiency and planning, by learning a model of the environment's dynamics. However, learning a global model that can generalize across different dynamics is a challenging task. To tackle this problem, we decompose the task of learning a global dynamics model into two stages: (a) learning a context latent vector that captures the local dynamics, then (b) predicting the next state conditioned on it. In order to encode dynamics-specific information into the context latent vector, we introduce a novel loss function that encourages the context latent vector to be useful for predicting both forward and backward dynamics. The proposed method achieves superior generalization ability across various simulated robotics and control tasks, compared to existing RL schemes.
Rolling Horizon NEAT for General Video Game Playing
Authors: Diego Perez-Liebana, Muhammad Sajid Alam, Raluca D. Gaina
Date: 2020-05-14 00:00:00

This paper presents a new Statistical Forward Planning (SFP) method, Rolling Horizon NeuroEvolution of Augmenting Topologies (rhNEAT). Unlike traditional Rolling Horizon Evolution, where an evolutionary algorithm is in charge of evolving a sequence of actions, rhNEAT evolves weights and connections of a neural network in real-time, planning several steps ahead before returning an action to execute in the game. Different versions of the algorithm are explored in a collection of 20 GVGAI games, and compared with other SFP methods and state of the art results. Although results are overall not better than other SFP methods, the nature of rhNEAT to adapt to changing game features has allowed to establish new state of the art records in games that other methods have traditionally struggled with. The algorithm proposed here is general and introduces a new way of representing information within rolling horizon evolution techniques.
Patient Similarity Analysis with Longitudinal Health Data
Authors: Ahmed Allam, Matthias Dittberner, Anna Sintsova, Dominique Brodbeck, Michael Krauthammer
Date: 2020-05-14 00:00:00
Tasks: Decision Making

Healthcare professionals have long envisioned using the enormous processing powers of computers to discover new facts and medical knowledge locked inside electronic health records. These vast medical archives contain time-resolved information about medical visits, tests and procedures, as well as outcomes, which together form individual patient journeys. By assessing the similarities among these journeys, it is possible to uncover clusters of common disease trajectories with shared health outcomes. The assignment of patient journeys to specific clusters may in turn serve as the basis for personalized outcome prediction and treatment selection. This procedure is a non-trivial computational problem, as it requires the comparison of patient data with multi-dimensional and multi-modal features that are captured at different times and resolutions. In this review, we provide a comprehensive overview of the tools and methods that are used in patient similarity analysis with longitudinal data and discuss its potential for improving clinical decision making.
Large Scale Font Independent Urdu Text Recognition System
Authors: Atique ur Rehman, Sibt Ul Hussain
Date: 2020-05-14 00:00:00
Tasks: Incremental Learning, Optical Character Recognition (OCR)

OCR algorithms have received a significant improvement in performance recently, mainly due to the increase in the capabilities of artificial intelligence algorithms. However, this advancement is not evenly distributed over all languages. Urdu is among the languages which did not receive much attention, especially in the font independent perspective. There exists no automated system that can reliably recognize printed Urdu text in images and videos across different fonts. To help bridge this gap, we have developed Qaida, a large scale data set with 256 fonts, and a complete Urdu lexicon. We have also developed a Convolutional Neural Network (CNN) based classification model which can recognize Urdu ligatures with 84.2% accuracy. Moreover, we demonstrate that our recognition network can not only recognize the text in the fonts it is trained on but can also reliably recognize text in unseen (new) fonts. To this end, this paper makes following contributions: (i) we introduce a large scale, multiple fonts based data set for printed Urdu text recognition;(ii) we have designed, trained and evaluated a CNN based model for Urdu text recognition; (iii) we experiment with incremental learning methods to produce state-of-the-art results for Urdu text recognition. All the experiment choices were thoroughly validated via detailed empirical analysis. We believe that this study can serve as the basis for further improvement in the performance of font independent Urdu OCR systems.
ECIR 2020 Workshops: Assessing the Impact of Going Online
Authors: Sérgio Nunes, Suzanne Little, Sumit Bhatia, Ludovico Boratto, Guillaume Cabanac, Ricardo Campos, Francisco M. Couto, Stefano Faralli, Ingo Frommholz, Adam Jatowt, Alípio Jorge, Mirko Marras, Philipp Mayr, Giovanni Stilo
Date: 2020-05-14 00:00:00

ECIR 2020 https://ecir2020.org/ was one of the many conferences affected by the COVID-19 pandemic. The Conference Chairs decided to keep the initially planned dates (April 14-17, 2020) and move to a fully online event. In this report, we describe the experience of organizing the ECIR 2020 Workshops in this scenario from two perspectives: the workshop organizers and the workshop participants. We provide a report on the organizational aspect of these events and the consequences for participants. Covering the scientific dimension of each workshop is outside the scope of this article.
The Information & Mutual Information Ratio for Counting Image Features and Their Matches
Authors: Ali Khajegili Mirabadi, Stefano Rini
Date: 2020-05-14 00:00:00
Tasks: Image Reconstruction

Feature extraction and description is an important topic of computer vision, as it is the starting point of a number of tasks such as image reconstruction, stitching, registration, and recognition among many others. In this paper, two new image features are proposed: the Information Ratio (IR) and the Mutual Information Ratio (MIR). The IR is a feature of a single image, while the MIR describes features common across two or more images.We begin by introducing the IR and the MIR and motivate these features in an information theoretical context as the ratio of the self-information of an intensity level over the information contained over the pixels of the same intensity. Notably, the relationship of the IR and MIR with the image entropy and mutual information, classic information measures, are discussed. Finally, the effectiveness of these features is tested through feature extraction over INRIA Copydays datasets and feature matching over the Oxfords Affine Covariant Regions. These numerical evaluations validate the relevance of the IR and MIR in practical computer vision tasks
Dense-Resolution Network for Point Cloud Classification and Segmentation
Authors: Shi Qiu, Saeed Anwar, Nick Barnes
Date: 2020-05-14 00:00:00
Tasks: 3D Part Segmentation, 3D Point Cloud Classification, Classification, General Classification, Point Cloud Classification, Point Cloud Segmentation

Point cloud analysis is attracting attention from Artificial Intelligence research since it can be widely used in applications such as robotics, Augmented Reality, self-driving. However, it is always challenging due to irregularities, unorderedness, and sparsity. In this article, we propose a novel network named Dense-Resolution Network (DRNet) for point cloud analysis. Our DRNet is designed to learn local point features from the point cloud in different resolutions. In order to learn local point groups more effectively, we present a novel grouping method for local neighborhood searching and an error-minimizing module for capturing local features. In addition to validating the network on widely used point cloud segmentation and classification benchmarks, we also test and visualize the performance of the components. Comparing with other state-of-the-art methods, our network shows superiority on ModelNet40, ShapeNet synthetic and ScanObjectNN real point cloud datasets.
Data Augmentation for Deep Candlestick Learner
Authors: Chia-Ying Tsao, Jun-Hao Chen, Samuel Yen-Chi Chen, Yun-Cheng Tsai
Date: 2020-05-14 00:00:00
Tasks: Data Augmentation

To successfully build a deep learning model, it will need a large amount of labeled data. However, labeled data are hard to collect in many use cases. To tackle this problem, a bunch of data augmentation methods have been introduced recently and have demonstrated successful results in computer vision, natural language and so on. For financial trading data, to our best knowledge, successful data augmentation framework has rarely been studied. Here we propose a Modified Local Search Attack Sampling method to augment the candlestick data, which is a very important tool for professional trader. Our results show that the proposed method can generate high-quality data which are hard to distinguish by human and will open a new way for finance community to employ existing machine learning techniques even if the dataset is small.
OD-SGD: One-step Delay Stochastic Gradient Descent for Distributed Training
Authors: Yemao Xu, Dezun Dong, Weixia Xu, Xiangke Liao
Date: 2020-05-14 00:00:00

The training of modern deep learning neural network calls for large amounts of computation, which is often provided by GPUs or other specific accelerators. To scale out to achieve faster training speed, two update algorithms are mainly applied in the distributed training process, i.e. the Synchronous SGD algorithm (SSGD) and Asynchronous SGD algorithm (ASGD). SSGD obtains good convergence point while the training speed is slowed down by the synchronous barrier. ASGD has faster training speed but the convergence point is lower when compared to SSGD. To sufficiently utilize the advantages of SSGD and ASGD, we propose a novel technology named One-step Delay SGD (OD-SGD) to combine their strengths in the training process. Therefore, we can achieve similar convergence point and training speed as SSGD and ASGD separately. To the best of our knowledge, we make the first attempt to combine the features of SSGD and ASGD to improve distributed training performance. Each iteration of OD-SGD contains a global update in the parameter server node and local updates in the worker nodes, the local update is introduced to update and compensate the delayed local weights. We evaluate our proposed algorithm on MNIST, CIFAR-10 and ImageNet datasets. Experimental results show that OD-SGD can obtain similar or even slightly better accuracy than SSGD, while its training speed is much faster, which even exceeds the training speed of ASGD.