pwc-archive/papers-with-abstracts huggingface dataset:
Abnormal Client Behavior Detection in Federated Learning
Authors: Suyi Li, Yong Cheng, Yang Liu, Wei Wang, Tianjian Chen
Date: 2019-10-22 00:00:00
Tasks: Anomaly Detection, Federated Learning, image-classification, Image Classification

In federated learning systems, clients are autonomous in that their behaviors are not fully governed by the server. Consequently, a client may intentionally or unintentionally deviate from the prescribed course of federated model training, resulting in abnormal behaviors, such as turning into a malicious attacker or a malfunctioning client. Timely detecting those anomalous clients is therefore critical to minimize their adverse impacts. In this work, we propose to detect anomalous clients at the server side. In particular, we generate low-dimensional surrogates of model weight vectors and use them to perform anomaly detection. We evaluate our solution through experiments on image classification model training over the FEMNIST dataset. Experimental results show that the proposed detection-based approach significantly outperforms the conventional defense-based methods.
Improving Transformer-based Speech Recognition Using Unsupervised Pre-training
Authors: Dongwei Jiang, Xiaoning Lei, Wubo Li, Ne Luo, Yuxuan Hu, Wei Zou, Xiangang Li
Date: 2019-10-22 00:00:00
Tasks: speech-recognition, Speech Recognition, Unsupervised Pre-training

Speech recognition technologies are gaining enormous popularity in various industrial applications. However, building a good speech recognition system usually requires large amounts of transcribed data, which is expensive to collect. To tackle this problem, an unsupervised pre-training method called Masked Predictive Coding is proposed, which can be applied for unsupervised pre-training with Transformer based model. Experiments on HKUST show that using the same training data, we can achieve CER 23.3%, exceeding the best end-to-end model by over 0.2% absolute CER. With more pre-training data, we can further reduce the CER to 21.0%, or a 11.8% relative CER reduction over baseline.
4-Connected Shift Residual Networks
Authors: Andrew Brown, Pascal Mettes, Marcel Worring
Date: 2019-10-22 00:00:00

The shift operation was recently introduced as an alternative to spatial convolutions. The operation moves subsets of activations horizontally and/or vertically. Spatial convolutions are then replaced with shift operations followed by point-wise convolutions, significantly reducing computational costs. In this work, we investigate how shifts should best be applied to high accuracy CNNs. We apply shifts of two different neighbourhood groups to ResNet on ImageNet: the originally introduced 8-connected (8C) neighbourhood shift and the less well studied 4-connected (4C) neighbourhood shift. We find that when replacing ResNet's spatial convolutions with shifts, both shift neighbourhoods give equal ImageNet accuracy, showing the sufficiency of small neighbourhoods for large images. Interestingly, when incorporating shifts to all point-wise convolutions in residual networks, 4-connected shifts outperform 8-connected shifts. Such a 4-connected shift setup gives the same accuracy as full residual networks while reducing the number of parameters and FLOPs by over 40%. We then highlight that without spatial convolutions, ResNet's downsampling/upsampling bottleneck channel structure is no longer needed. We show a new, 4C shift-based residual network, much shorter than the original ResNet yet with a higher accuracy for the same computational cost. This network is the highest accuracy shift-based network yet shown, demonstrating the potential of shifting in deep neural networks.
Weakly-Supervised Completion Moment Detection using Temporal Attention
Authors: Farnoosh Heidarivincheh, Majid Mirmehdi, Dima Damen
Date: 2019-10-22 00:00:00

Monitoring the progression of an action towards completion offers fine grained insight into the actor's behaviour. In this work, we target detecting the completion moment of actions, that is the moment when the action's goal has been successfully accomplished. This has potential applications from surveillance to assistive living and human-robot interactions. Previous effort required human annotations of the completion moment for training (i.e. full supervision). In this work, we present an approach for moment detection from weak video-level labels. Given both complete and incomplete sequences, of the same action, we learn temporal attention, along with accumulated completion prediction from all frames in the sequence. We also demonstrate how the approach can be used when completion moment supervision is available. We evaluate and compare our approach on actions from three datasets, namely HMDB, UCF101 and RGBD-AC, and show that temporal attention improves detection in both weakly-supervised and fully-supervised settings.
Automatic Extraction of Personality from Text: Challenges and Opportunities
Authors: Nazar Akrami, Johan Fernquist, Tim Isbister, Lisa Kaati, Björn Pelzer
Date: 2019-10-22 00:00:00
Tasks: Language Modeling, Language Modelling

In this study, we examined the possibility to extract personality traits from a text. We created an extensive dataset by having experts annotate personality traits in a large number of texts from multiple online sources. From these annotated texts, we selected a sample and made further annotations ending up in a large low-reliability dataset and a small high-reliability dataset. We then used the two datasets to train and test several machine learning models to extract personality from text, including a language model. Finally, we evaluated our best models in the wild, on datasets from different domains. Our results show that the models based on the small high-reliability dataset performed better (in terms of $\textrm{R}^2$) than models based on large low-reliability dataset. Also, language model based on small high-reliability dataset performed better than the random baseline. Finally, and more importantly, the results showed our best model did not perform better than the random baseline when tested in the wild. Taken together, our results show that determining personality traits from a text remains a challenge and that no firm conclusions can be made on model performance before testing in the wild.
WeatherNet: Recognising weather and visual conditions from street-level images using deep residual learning
Authors: Mohamed R. Ibrahim, James Haworth, Tao Cheng
Date: 2019-10-22 00:00:00
Tasks: Autonomous Vehicles

Extracting information related to weather and visual conditions at a given time and space is indispensable for scene awareness, which strongly impacts our behaviours, from simply walking in a city to riding a bike, driving a car, or autonomous drive-assistance. Despite the significance of this subject, it is still not been fully addressed by the machine intelligence relying on deep learning and computer vision to detect the multi-labels of weather and visual conditions with a unified method that can be easily used for practice. What has been achieved to-date is rather sectorial models that address limited number of labels that do not cover the wide spectrum of weather and visual conditions. Nonetheless, weather and visual conditions are often addressed individually. In this paper, we introduce a novel framework to automatically extract this information from street-level images relying on deep learning and computer vision using a unified method without any pre-defined constraints in the processed images. A pipeline of four deep Convolutional Neural Network (CNN) models, so-called the WeatherNet, is trained, relying on residual learning using ResNet50 architecture, to extract various weather and visual conditions such as Dawn/dusk, day and night for time detection, and glare for lighting conditions, and clear, rainy, snowy, and foggy for weather conditions. The WeatherNet shows strong performance in extracting this information from user-defined images or video streams that can be used not limited to: autonomous vehicles and drive-assistance systems, tracking behaviours, safety-related research, or even for better understanding cities through images for policy-makers.
Word-level Embeddings for Cross-Task Transfer Learning in Speech Processing
Authors: Pierre Beckmann, Mikolaj Kegler, Milos Cernak
Date: 2019-10-22 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Identification, Music Classification, Representation Learning, Speaker Identification, Speech Enhancement, speech-recognition, Speech Recognition, Transfer Learning

Recent breakthroughs in deep learning often rely on representation learning and knowledge transfer. In recent years, unsupervised and self-supervised techniques for learning speech representation were developed to foster automatic speech recognition. Up to date, most of these approaches are task-specific and designed for within-task transfer learning between different datasets or setups of a particular task. In turn, learning task-independent representation of speech and cross-task applications of transfer learning remain less common. Here, we introduce an encoder capturing word-level representations of speech for cross-task transfer learning. We demonstrate the application of the pre-trained encoder in four distinct speech and audio processing tasks: (i) speech enhancement, (ii) language identification, (iii) speech, noise, and music classification, and (iv) speaker identification. In each task, we compare the performance of our cross-task transfer learning approach to task-specific baselines. Our results show that the speech representation captured by the encoder through the pre-training is transferable across distinct speech processing tasks and datasets. Notably, even simple applications of our pre-trained encoder outperformed task-specific methods, or were comparable, depending on the task.
A Locating Model for Pulmonary Tuberculosis Diagnosis in Radiographs
Authors: Jiwei Liu, Junyu Liu, Yang Liu, Rui Yang, Dongjun Lv, Zhengting Cai, Jingjing Cui
Date: 2019-10-22 00:00:00
Tasks: Diagnostic, Position regression

Objective: We propose an end-to-end CNN-based locating model for pulmonary tuberculosis (TB) diagnosis in radiographs. This model makes full use of chest radiograph (X-ray) for its improved accessibility, reduced cost and high accuracy for TB disease. Methods: Several specialized improvements are proposed for detection task in medical field. A false positive (FP) restrictor head is introduced for FP reduction. Anchor-oriented network heads is proposed in the position regression section. An optimization of loss function is designed for hard example mining. Results: The experimental results show that when the threshold of intersection over union (IoU) is set to 0.3, the average precision (AP) of two test data sets provided by different hospitals reaches 0.9023 and 0.9332. Ablation experiments shows that hard example mining and change of regressor heads contribute most in this work, but FP restriction is necessary in a CAD diagnose system. Conclusion: The results prove the high precision and good generalization ability of our proposed model comparing to previous works. Significance: We first make full use of the feature extraction ability of CNNs in TB diagnostic field and make exploration in localization of TB, when the previous works focus on the weaker task of healthy-sick subject classification.
Improving the Gating Mechanism of Recurrent Neural Networks
Authors: Albert Gu, Caglar Gulcehre, Tom Le Paine, Matt Hoffman, Razvan Pascanu
Date: 2019-10-22 00:00:00
Proceeding: ICML 2020 1
Tasks: image-classification, Image Classification, Language Modeling, Language Modelling, Memorization, Reinforcement Learning, Sequential Image Classification

Gating mechanisms are widely used in neural network models, where they allow gradients to backpropagate more easily through depth or time. However, their saturation property introduces problems of its own. For example, in recurrent models these gates need to have outputs near 1 to propagate information over long time-delays, which requires them to operate in their saturation regime and hinders gradient-based learning of the gate mechanism. We address this problem by deriving two synergistic modifications to the standard gating mechanism that are easy to implement, introduce no additional hyperparameters, and improve learnability of the gates when they are close to saturation. We show how these changes are related to and improve on alternative recently proposed gating mechanisms such as chrono initialization and Ordered Neurons. Empirically, our simple gating mechanisms robustly improve the performance of recurrent models on a range of applications, including synthetic memorization tasks, sequential image classification, language modeling, and reinforcement learning, particularly when long-term dependencies are involved.
Kernel computations from large-scale random features obtained by Optical Processing Units
Authors: Ruben Ohana, Jonas Wacker, Jonathan Dong, Sébastien Marmin, Florent Krzakala, Maurizio Filippone, Laurent Daudet
Date: 2019-10-22 00:00:00
Tasks: image-classification, Image Classification, Transfer Learning

Approximating kernel functions with random features (RFs)has been a successful application of random projections for nonparametric estimation. However, performing random projections presents computational challenges for large-scale problems. Recently, a new optical hardware called Optical Processing Unit (OPU) has been developed for fast and energy-efficient computation of large-scale RFs in the analog domain. More specifically, the OPU performs the multiplication of input vectors by a large random matrix with complex-valued i.i.d. Gaussian entries, followed by the application of an element-wise squared absolute value operation - this last nonlinearity being intrinsic to the sensing process. In this paper, we show that this operation results in a dot-product kernel that has connections to the polynomial kernel, and we extend this computation to arbitrary powers of the feature map. Experiments demonstrate that the OPU kernel and its RF approximation achieve competitive performance in applications using kernel ridge regression and transfer learning for image classification. Crucially, thanks to the use of the OPU, these results are obtained with time and energy savings.
Towards Combinational Relation Linking over Knowledge Graphs
Authors: Weiguo Zheng, Mei Zhang
Date: 2019-10-22 00:00:00
Tasks: Knowledge Graphs, Question Answering, Relation, Relation Linking, Text Summarization

Given a natural language phrase, relation linking aims to find a relation (predicate or property) from the underlying knowledge graph to match the phrase. It is very useful in many applications, such as natural language question answering, personalized recommendation and text summarization. However, the previous relation linking algorithms usually produce a single relation for the input phrase and pay little attention to a more general and challenging problem, i.e., combinational relation linking that extracts a subgraph pattern to match the compound phrase (e.g. mother-in-law). In this paper, we focus on the task of combinational relation linking over knowledge graphs. To resolve the problem, we design a systematic method based on the data-driven relation assembly technique, which is performed under the guidance of meta patterns. We also introduce external knowledge to enhance the system understanding ability. Finally, we conduct extensive experiments over the real knowledge graph to study the performance of the proposed method.
Neural Network Training with Approximate Logarithmic Computations
Authors: Arnab Sanyal, Peter A. Beerel, Keith M. Chugg
Date: 2019-10-22 00:00:00

The high computational complexity associated with training deep neural networks limits online and real-time training on edge devices. This paper proposed an end-to-end training and inference scheme that eliminates multiplications by approximate operations in the log-domain which has the potential to significantly reduce implementation complexity. We implement the entire training procedure in the log-domain, with fixed-point data representations. This training procedure is inspired by hardware-friendly approximations of log-domain addition which are based on look-up tables and bit-shifts. We show that our 16-bit log-based training can achieve classification accuracy within approximately 1% of the equivalent floating-point baselines for a number of commonly used datasets.
A Prototypical Triplet Loss for Cover Detection
Authors: Guillaume Doras, Geoffroy Peeters
Date: 2019-10-22 00:00:00
Tasks: Clustering, Triplet

Automatic cover detection -- the task of finding in a audio dataset all covers of a query track -- has long been a challenging theoretical problem in MIR community. It also became a practical need for music composers societies requiring to detect automatically if an audio excerpt embeds musical content belonging to their catalog. In a recent work, we addressed this problem with a convolutional neural network mapping each track's dominant melody to an embedding vector, and trained to minimize cover pairs distance in the embeddings space, while maximizing it for non-covers. We showed in particular that training this model with enough works having five or more covers yields state-of-the-art results. This however does not reflect the realistic use case, where music catalogs typically contain works with zero or at most one or two covers. We thus introduce here a new test set incorporating these constraints, and propose two contributions to improve our model's accuracy under these stricter conditions: we replace dominant melody with multi-pitch representation as input data, and describe a novel prototypical triplet loss designed to improve covers clustering. We show that these changes improve results significantly for two concrete use cases, large dataset lookup and live songs identification.
Fixed Pattern Noise Reduction for Infrared Images Based on Cascade Residual Attention CNN
Authors: Juntao Guan, Rui Lai, Ai Xiong, Zesheng Liu, Lin Gu
Date: 2019-10-22 00:00:00

Existing fixed pattern noise reduction (FPNR) methods are easily affected by the motion state of the scene and working condition of the image sensor, which leads to over smooth effects, ghosting artifacts as well as slow convergence rate. To address these issues, we design an innovative cascade convolution neural network (CNN) model with residual skip connections to realize single frame blind FPNR operation without any parameter tuning. Moreover, a coarse-fine convolution (CF-Conv) unit is introduced to extract complementary features in various scales and fuse them to pick more spatial information. Inspired by the success of the visual attention mechanism, we further propose a particular spatial-channel noise attention unit (SCNAU) to separate the scene details from fixed pattern noise more thoroughly and recover the real scene more accurately. Experimental results on test data demonstrate that the proposed cascade CNN-FPNR method outperforms the existing FPNR methods in both of visual effect and quantitative assessment.
An Efficient and Effective Second-Order Training Algorithm for LSTM-based Adaptive Learning
Authors: N. Mert Vural, Salih Ergüt, Suleyman S. Kozat
Date: 2019-10-22 00:00:00
Tasks: regression

We study adaptive (or online) nonlinear regression with Long-Short-Term-Memory (LSTM) based networks, i.e., LSTM-based adaptive learning. In this context, we introduce an efficient Extended Kalman filter (EKF) based second-order training algorithm. Our algorithm is truly online, i.e., it does not assume any underlying data generating process and future information, except that the target sequence is bounded. Through an extensive set of experiments, we demonstrate significant performance gains achieved by our algorithm with respect to the state-of-the-art methods. Here, we mainly show that our algorithm consistently provides 10 to 45\% improvement in the accuracy compared to the widely-used adaptive methods Adam, RMSprop, and DEKF, and comparable performance to EKF with a 10 to 15 times reduction in the run-time.
Orthogonal variance decomposition based feature selection
Authors: Firuz Kamalov
Date: 2019-10-22 00:00:00
Tasks: feature selection

Existing feature selection methods fail to properly account for interactions between features when evaluating feature subsets. In this paper, we attempt to remedy this issue by using orthogonal variance decomposition to evaluate features. The orthogonality of the decomposition allows us to directly calculate the total contribution of a feature to the output variance. Thus we obtain an efficient algorithm for feature evaluation which takes into account interactions among features. Numerical experiments demonstrate that our method accurately identifies relevant features and improves the accuracy of numerical models.
Towards Best Practice in Explaining Neural Network Decisions with LRP
Authors: Maximilian Kohlbrenner, Alexander Bauer, Shinichi Nakajima, Alexander Binder, Wojciech Samek, Sebastian Lapuschkin
Date: 2019-10-22 00:00:00
Tasks: Explainable artificial intelligence, Explainable Artificial Intelligence (XAI), object-detection, Object Detection, Object Localization

Within the last decade, neural network based predictors have demonstrated impressive - and at times super-human - capabilities. This performance is often paid for with an intransparent prediction process and thus has sparked numerous contributions in the novel field of explainable artificial intelligence (XAI). In this paper, we focus on a popular and widely used method of XAI, the Layer-wise Relevance Propagation (LRP). Since its initial proposition LRP has evolved as a method, and a best practice for applying the method has tacitly emerged, based however on humanly observed evidence alone. In this paper we investigate - and for the first time quantify - the effect of this current best practice on feedforward neural networks in a visual object detection setting. The results verify that the layer-dependent approach to LRP applied in recent literature better represents the model's reasoning, and at the same time increases the object localization and class discriminativity of LRP.
Hetero-Center Loss for Cross-Modality Person Re-Identification
Authors: Yuanxin Zhu, Zhao Yang, Li Wang, Sai Zhao, Xiao Hu, Dapeng Tao
Date: 2019-10-22 00:00:00
Tasks: Cross-Modality Person Re-identification, Person Re-Identification

Cross-modality person re-identification is a challenging problem which retrieves a given pedestrian image in RGB modality among all the gallery images in infrared modality. The task can address the limitation of RGB-based person Re-ID in dark environments. Existing researches mainly focus on enlarging inter-class differences of feature to solve the problem. However, few studies investigate improving intra-class cross-modality similarity, which is important for this issue. In this paper, we propose a novel loss function, called Hetero-Center loss (HC loss) to reduce the intra-class cross-modality variations. Specifically, HC loss can supervise the network learning the cross-modality invariant information by constraining the intra-class center distance between two heterogenous modalities. With the joint supervision of Cross-Entropy (CE) loss and HC loss, the network is trained to achieve two vital objectives, inter-class discrepancy and intra-class cross-modality similarity as much as possible. Besides, we propose a simple and high-performance network architecture to learn local feature representations for cross-modality person re-identification, which can be a baseline for future research. Extensive experiments indicate the effectiveness of the proposed methods, which outperform state-of-the-art methods by a wide margin.