pwc-archive/papers-with-abstracts huggingface dataset:
AdvKnn: Adversarial Attacks On K-Nearest Neighbor Classifiers With Approximate Gradients
Authors: Xiaodan Li, Yuefeng Chen, Yuan He, Hui Xue
Date: 2019-11-15 00:00:00
Tasks: Adversarial Robustness

Deep neural networks have been shown to be vulnerable to adversarial examples---maliciously crafted examples that can trigger the target model to misbehave by adding imperceptible perturbations. Existing attack methods for k-nearest neighbor~(kNN) based algorithms either require large perturbations or are not applicable for large k. To handle this problem, this paper proposes a new method called AdvKNN for evaluating the adversarial robustness of kNN-based models. Firstly, we propose a deep kNN block to approximate the output of kNN methods, which is differentiable thus can provide gradients for attacks to cross the decision boundary with small distortions. Second, a new consistency learning for distribution instead of classification is proposed for the effectiveness in distribution based methods. Extensive experimental results indicate that the proposed method significantly outperforms state of the art in terms of attack success rate and the added perturbations.
Learning To Characterize Adversarial Subspaces
Authors: Xiaofeng Mao, Yuefeng Chen, Yuhong Li, Yuan He, Hui Xue
Date: 2019-11-15 00:00:00

Deep Neural Networks (DNNs) are known to be vulnerable to the maliciously generated adversarial examples. To detect these adversarial examples, previous methods use artificially designed metrics to characterize the properties of \textit{adversarial subspaces} where adversarial examples lie. However, we find these methods are not working in practical attack detection scenarios. Because the artificially defined features are lack of robustness and show limitation in discriminative power to detect strong attacks. To solve this problem, we propose a novel adversarial detection method which identifies adversaries by adaptively learning reasonable metrics to characterize adversarial subspaces. As auxiliary context information, \textit{k} nearest neighbors are used to represent the surrounded subspace of the detected sample. We propose an innovative model called Neighbor Context Encoder (NCE) to learn from \textit{k} neighbors context and infer if the detected sample is normal or adversarial. We conduct thorough experiment on CIFAR-10, CIFAR-100 and ImageNet dataset. The results demonstrate that our approach surpasses all existing methods under three settings: \textit{attack-aware black-box detection}, \textit{attack-unaware black-box detection} and \textit{white-box detection}.
Independent and automatic evaluation of acoustic-to-articulatory inversion models
Authors: Parrot Maud, Millet Juliette, Dunbar Ewan
Date: 2019-11-15 00:00:00
Tasks: speech-recognition, Speech Recognition, Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

Reconstruction of articulatory trajectories from the acoustic speech signal has been proposed for improving speech recognition and text-to-speech synthesis. However, to be useful in these settings, articulatory reconstruction must be speaker independent. Furthermore, as most research focuses on single, small datasets with few speakers, robust articulatory reconstrucion could profit from combining datasets. Standard evaluation measures such as root mean square error and Pearson correlation are inappropriate for evaluating the speaker-independence of models or the usefulness of combining datasets. We present a new evaluation for articulatory reconstruction which is independent of the articulatory data set used for training: the phone discrimination ABX task. We use the ABX measure to evaluate a Bi-LSTM based model trained on 3 datasets (14 speakers), and show that it gives information complementary to the standard measures, and enables us to evaluate the effects of dataset merging, as well as the speaker independence of the model.
Multi-Label Learning with Deep Forest
Authors: Liang Yang, Xi-Zhu Wu, Yuan Jiang, Zhi-Hua Zhou
Date: 2019-11-15 00:00:00
Tasks: Multi-Label Learning

In multi-label learning, each instance is associated with multiple labels and the crucial task is how to leverage label correlations in building models. Deep neural network methods usually jointly embed the feature and label information into a latent space to exploit label correlations. However, the success of these methods highly depends on the precise choice of model depth. Deep forest is a recent deep learning framework based on tree model ensembles, which does not rely on backpropagation. We consider the advantages of deep forest models are very appropriate for solving multi-label problems. Therefore we design the Multi-Label Deep Forest (MLDF) method with two mechanisms: measure-aware feature reuse and measure-aware layer growth. The measure-aware feature reuse mechanism reuses the good representation in the previous layer guided by confidence. The measure-aware layer growth mechanism ensures MLDF gradually increase the model complexity by performance measure. MLDF handles two challenging problems at the same time: one is restricting the model complexity to ease the overfitting issue; another is optimizing the performance measure on user's demand since there are many different measures in the multi-label evaluation. Experiments show that our proposal not only beats the compared methods over six measures on benchmark datasets but also enjoys label correlation discovery and other desired properties in multi-label learning.
Safe Interactive Model-Based Learning
Authors: Marco Gallieri, Seyed Sina Mirrazavi Salehian, Nihat Engin Toklu, Alessio Quaglino, Jonathan Masci, Jan Koutník, Faustino Gomez
Date: 2019-11-15 00:00:00
Tasks: model, Safe Exploration

Control applications present hard operational constraints. A violation of these can result in unsafe behavior. This paper introduces Safe Interactive Model Based Learning (SiMBL), a framework to refine an existing controller and a system model while operating on the real environment. SiMBL is composed of the following trainable components: a Lyapunov function, which determines a safe set; a safe control policy; and a Bayesian RNN forward model. A min-max control framework, based on alternate minimisation and backpropagation through the forward model, is used for the offline computation of the controller and the safe set. Safety is formally verified a-posteriori with a probabilistic method that utilizes the Noise Contrastive Priors (NPC) idea to build a Bayesian RNN forward model with an additive state uncertainty estimate which is large outside the training data distribution. Iterative refinement of the model and the safe set is achieved thanks to a novel loss that conditions the uncertainty estimates of the new model to be close to the current one. The learned safe set and model can also be used for safe exploration, i.e., to collect data within the safe invariant set, for which a simple one-step MPC is proposed. The single components are tested on the simulation of an inverted pendulum with limited torque and stability region, showing that iteratively adding more data can improve the model, the controller and the size of the safe region.
Fine-grained Qualitative Spatial Reasoning about Point Positions
Authors: Sören Schwertfeger
Date: 2019-11-15 00:00:00
Tasks: Spatial Reasoning

The ability to persist in the spacial environment is, not only in the robotic context, an essential feature. Positional knowledge is one of the most important aspects of space and a number of methods to represent these information have been developed in the in the research area of spatial cognition. The basic qualitative spatial representation and reasoning techniques are presented in this thesis and several calculi are briefly reviewed. Features and applications of qualitative calculi are summarized. A new calculus for representing and reasoning about qualitative spatial orientation and distances is being designed. It supports an arbitrary level of granularity over ternary relations of points. Ways of improving the complexity of the composition are shown and an implementation of the calculus demonstrates its capabilities. Existing qualitative spatial calculi of positional information are compared to the new approach and possibilities for future research are outlined.
LIBRE: Learning Interpretable Boolean Rule Ensembles
Authors: Graziano Mita, Paolo Papotti, Maurizio Filippone, Pietro Michiardi
Date: 2019-11-15 00:00:00

We present a novel method - LIBRE - to learn an interpretable classifier, which materializes as a set of Boolean rules. LIBRE uses an ensemble of bottom-up weak learners operating on a random subset of features, which allows for the learning of rules that generalize well on unseen data even in imbalanced settings. Weak learners are combined with a simple union so that the final ensemble is also interpretable. Experimental results indicate that LIBRE efficiently strikes the right balance between prediction accuracy, which is competitive with black box methods, and interpretability, which is often superior to alternative methods from the literature.
A3GAN: An Attribute-aware Attentive Generative Adversarial Network for Face Aging
Authors: Yunfan Liu, Qi Li, Zhenan Sun, Tieniu Tan
Date: 2019-11-15 00:00:00
Tasks: Attribute, Generative Adversarial Network

Face aging, which aims at aesthetically rendering a given face to predict its future appearance, has received significant research attention in recent years. Although great progress has been achieved with the success of Generative Adversarial Networks (GANs) in synthesizing realistic images, most existing GAN-based face aging methods have two main problems: 1) unnatural changes of high-level semantic information (e.g. facial attributes) due to the insufficient utilization of prior knowledge of input faces, and 2) distortions of low-level image content including ghosting artifacts and modifications in age-irrelevant regions. In this paper, we introduce A3GAN, an Attribute-Aware Attentive face aging model to address the above issues. Facial attribute vectors are regarded as the conditional information and embedded into both the generator and discriminator, encouraging synthesized faces to be faithful to attributes of corresponding inputs. To improve the visual fidelity of generation results, we leverage the attention mechanism to restrict modifications to age-related areas and preserve image details. Moreover, the wavelet packet transform is employed to capture textural features at multiple scales in the frequency space. Extensive experimental results demonstrate the effectiveness of our model in synthesizing photorealistic aged face images and achieving state-of-the-art performance on popular face aging datasets.
Likelihood Assignment for Out-of-Distribution Inputs in Deep Generative Models is Sensitive to Prior Distribution Choice
Authors: Ryo Kamoi, Kei Kobayashi
Date: 2019-11-15 00:00:00

Recent work has shown that deep generative models assign higher likelihood to out-of-distribution inputs than to training data. We show that a factor underlying this phenomenon is a mismatch between the nature of the prior distribution and that of the data distribution, a problem found in widely used deep generative models such as VAEs and Glow. While a typical choice for a prior distribution is a standard Gaussian distribution, properties of distributions of real data sets may not be consistent with a unimodal prior distribution. This paper focuses on the relationship between the choice of a prior distribution and the likelihoods assigned to out-of-distribution inputs. We propose the use of a mixture distribution as a prior to make likelihoods assigned by deep generative models sensitive to out-of-distribution inputs. Furthermore, we explain the theoretical advantages of adopting a mixture distribution as the prior, and we present experimental results to support our claims. Finally, we demonstrate that a mixture prior lowers the out-of-distribution likelihood with respect to two pairs of real image data sets: Fashion-MNIST vs. MNIST and CIFAR10 vs. SVHN.
Improved algorithm for neuronal ensemble inference by Monte Carlo method
Authors: Shun Kimura, Koujin Takeda
Date: 2019-11-15 00:00:00
Tasks: Bayesian Inference

Neuronal ensemble inference is one of the significant problems in the study of biological neural networks. Various methods have been proposed for ensemble inference from their activity data taken experimentally. Here we focus on Bayesian inference approach for ensembles with generative model, which was proposed in recent work. However, this method requires large computational cost, and the result sometimes gets stuck in bad local maximum solution of Bayesian inference. In this work, we give improved Bayesian inference algorithm for these problems. We modify ensemble generation rule in Markov chain Monte Carlo method, and introduce the idea of simulated annealing for hyperparameter control. We also compare the performance of ensemble inference between our algorithm and the original one.
Single View Distortion Correction using Semantic Guidance
Authors: Szabolcs-Botond Lőrincz, Szabolcs Pável, Lehel Csató
Date: 2019-11-15 00:00:00
Tasks: distortion correction

Most distortion correction methods focus on simple forms of distortion, such as radial or linear distortions. These works undistort images either based on measurements in the presence of a calibration grid, or use multiple views to find point correspondences and predict distortion parameters. When possible distortions are more complex, e.g. in the case of a camera being placed behind a refractive surface such as glass, the standard method is to use a calibration grid. Considering a high variety of distortions, it is nonviable to conduct these measurements. In this work, we present a single view distortion correction method which is capable of undistorting images containing arbitrarily complex distortions by exploiting recent advancements in differentiable image sampling and in the usage of semantic information to augment various tasks. The results of this work show that our model is able to estimate and correct highly complex distortions, and that incorporating semantic information mitigates the process of image undistortion.
Simple iterative method for generating targeted universal adversarial perturbations
Authors: Hokuto Hirano, Kazuhiro Takemoto
Date: 2019-11-15 00:00:00
Tasks: General Classification, image-classification, Image Classification

Deep neural networks (DNNs) are vulnerable to adversarial attacks. In particular, a single perturbation known as the universal adversarial perturbation (UAP) can foil most classification tasks conducted by DNNs. Thus, different methods for generating UAPs are required to fully evaluate the vulnerability of DNNs. A realistic evaluation would be with cases that consider targeted attacks; wherein the generated UAP causes DNN to classify an input into a specific class. However, the development of UAPs for targeted attacks has largely fallen behind that of UAPs for non-targeted attacks. Therefore, we propose a simple iterative method to generate UAPs for targeted attacks. Our method combines the simple iterative method for generating non-targeted UAPs and the fast gradient sign method for generating a targeted adversarial perturbation for an input. We applied the proposed method to state-of-the-art DNN models for image classification and proved the existence of almost imperceptible UAPs for targeted attacks; further, we demonstrated that such UAPs are easily generatable.
Improving Distant Supervised Relation Extraction by Dynamic Neural Network
Authors: Yanjie Gou, Yinjie Lei, Lingqiao Liu, Pingping Zhang, Xi Peng
Date: 2019-11-15 00:00:00
Tasks: Relation, Relation Extraction

Distant Supervised Relation Extraction (DSRE) is usually formulated as a problem of classifying a bag of sentences that contain two query entities, into the predefined relation classes. Most existing methods consider those relation classes as distinct semantic categories while ignoring their potential connection to query entities. In this paper, we propose to leverage this connection to improve the relation extraction accuracy. Our key ideas are twofold: (1) For sentences belonging to the same relation class, the expression style, i.e. words choice, can vary according to the query entities. To account for this style shift, the model should adjust its parameters in accordance with entity types. (2) Some relation classes are semantically similar, and the entity types appear in one relation may also appear in others. Therefore, it can be trained cross different relation classes and further enhance those classes with few samples, i.e., long-tail classes. To unify these two arguments, we developed a novel Dynamic Neural Network for Relation Extraction (DNNRE). The network adopts a novel dynamic parameter generator that dynamically generates the network parameters according to the query entity types and relation classes. By using this mechanism, the network can simultaneously handle the style shift problem and enhance the prediction accuracy for long-tail classes. Through our experimental study, we demonstrate the effectiveness of the proposed method and show that it can achieve superior performance over the state-of-the-art methods.
Using natural language processing to extract health-related causality from Twitter messages
Authors: Son Doan, Elly W Yang, Sameer Tilak, Manabu Torii
Date: 2019-11-15 00:00:00

Twitter messages (tweets) contain various types of information, which include health-related information. Analysis of health-related tweets would help us understand health conditions and concerns encountered in our daily life. In this work, we evaluated an approach to extracting causal relations from tweets using natural language processing (NLP) techniques. We focused on three health-related topics: stress", "insomnia", and "headache". We proposed a set of lexico-syntactic patterns based on dependency parser outputs to extract causal information. A large dataset consisting of 24 million tweets were used. The results show that our approach achieved an average precision between 74.59% and 92.27%. Analysis of extracted relations revealed interesting findings about health-related in Twitter.
OpenLORIS-Object: A Robotic Vision Dataset and Benchmark for Lifelong Deep Learning
Authors: Qi She, Fan Feng, Xinyue Hao, Qihan Yang, Chuanlin Lan, Vincenzo Lomonaco, Xuesong Shi, Zhengwei Wang, Yao Guo, Yimin Zhang, Fei Qiao, Rosa H. M. Chan
Date: 2019-11-15 00:00:00
Tasks: Lifelong learning, Object, Object Recognition

The recent breakthroughs in computer vision have benefited from the availability of large representative datasets (e.g. ImageNet and COCO) for training. Yet, robotic vision poses unique challenges for applying visual algorithms developed from these standard computer vision datasets due to their implicit assumption over non-varying distributions for a fixed set of tasks. Fully retraining models each time a new task becomes available is infeasible due to computational, storage and sometimes privacy issues, while na\"{i}ve incremental strategies have been shown to suffer from catastrophic forgetting. It is crucial for the robots to operate continuously under open-set and detrimental conditions with adaptive visual perceptual systems, where lifelong learning is a fundamental capability. However, very few datasets and benchmarks are available to evaluate and compare emerging techniques. To fill this gap, we provide a new lifelong robotic vision dataset ("OpenLORIS-Object") collected via RGB-D cameras. The dataset embeds the challenges faced by a robot in the real-life application and provides new benchmarks for validating lifelong object recognition algorithms. Moreover, we have provided a testbed of $9$ state-of-the-art lifelong learning algorithms. Each of them involves $48$ tasks with $4$ evaluation metrics over the OpenLORIS-Object dataset. The results demonstrate that the object recognition task in the ever-changing difficulty environments is far from being solved and the bottlenecks are at the forward/backward transfer designs. Our dataset and benchmark are publicly available at at \href{https://lifelong-robotic-vision.github.io/dataset/object}{\underline{https://lifelong-robotic-vision.github.io/dataset/object}}.
Automated Augmentation with Reinforcement Learning and GANs for Robust Identification of Traffic Signs using Front Camera Images
Authors: Sohini Roy Chowdhury, Lars Tornberg, Robin Halvfordsson, Jonatan Nordh, Adam Suhren Gustafsson, Joel Wall, Mattias Westerberg, Adam Wirehed, Louis Tilloy, Zhanying Hu, Haoyuan Tan, Meng Pan, Jonas Sjoberg
Date: 2019-11-15 00:00:00
Tasks: Autonomous Driving, Generative Adversarial Network, Reinforcement Learning, Traffic Sign Detection

Traffic sign identification using camera images from vehicles plays a critical role in autonomous driving and path planning. However, the front camera images can be distorted due to blurriness, lighting variations and vandalism which can lead to degradation of detection performances. As a solution, machine learning models must be trained with data from multiple domains, and collecting and labeling more data in each new domain is time consuming and expensive. In this work, we present an end-to-end framework to augment traffic sign training data using optimal reinforcement learning policies and a variety of Generative Adversarial Network (GAN) models, that can then be used to train traffic sign detector modules. Our automated augmenter enables learning from transformed nightime, poor lighting, and varying degrees of occlusions using the LISA Traffic Sign and BDD-Nexar dataset. The proposed method enables mapping training data from one domain to another, thereby improving traffic sign detection precision/recall from 0.70/0.66 to 0.83/0.71 for nighttime images.
On Model Robustness Against Adversarial Examples
Authors: Shufei Zhang, Kai-Zhu Huang, Zenglin Xu
Date: 2019-11-15 00:00:00
Tasks: model

We study the model robustness against adversarial examples, referred to as small perturbed input data that may however fool many state-of-the-art deep learning models. Unlike previous research, we establish a novel theory addressing the robustness issue from the perspective of stability of the loss function in the small neighborhood of natural examples. We propose to exploit an energy function to describe the stability and prove that reducing such energy guarantees the robustness against adversarial examples. We also show that the traditional training methods including adversarial training with the $l_2$ norm constraint (AT) and Virtual Adversarial Training (VAT) tend to minimize the lower bound of our proposed energy function. We make an analysis showing that minimization of such lower bound can however lead to insufficient robustness within the neighborhood around the input sample. Furthermore, we design a more rational method with the energy regularization which proves to achieve better robustness than previous methods. Through a series of experiments, we demonstrate the superiority of our model on both supervised tasks and semi-supervised tasks. In particular, our proposed adversarial framework achieves the best performance compared with previous adversarial training methods on benchmark datasets MNIST, CIFAR-10, and SVHN. Importantly, they demonstrate much better robustness against adversarial examples than all the other comparison methods.