pwc-archive/papers-with-abstracts huggingface dataset:
Assisted Sound Sample Generation with Musical Conditioning in Adversarial Auto-Encoders
Authors: Adrien Bitton, Philippe Esling, Antoine Caillon, Martin Fouilleul
Date: 2019-04-12 00:00:00
Tasks: Attribute, Audio Generation, Decoder

Generative models have thrived in computer vision, enabling unprecedented image processes. Yet the results in audio remain less advanced. Our project targets real-time sound synthesis from a reduced set of high-level parameters, including semantic controls that can be adapted to different sound libraries and specific tags. These generative variables should allow expressive modulations of target musical qualities and continuously mix into new styles. To this extent we train AEs on an orchestral database of individual note samples, along with their intrinsic attributes: note class, timbre domain and extended playing techniques. We condition the decoder for control over the rendered note attributes and use latent adversarial training for learning expressive style parameters that can ultimately be mixed. We evaluate both generative performances and latent representation. Our ablation study demonstrates the effectiveness of the musical conditioning mechanisms. The proposed model generates notes as magnitude spectrograms from any probabilistic latent code samples, with expressive control of orchestral timbres and playing styles. Its training data subsets can directly be visualized in the 3D latent representation. Waveform rendering can be done offline with GLA. In order to allow real-time interactions, we fine-tune the decoder with a pretrained MCNN and embed the full waveform generation pipeline in a plugin. Moreover the encoder could be used to process new input samples, after manipulating their latent attribute representation, the decoder can generate sample variations as an audio effect would. Our solution remains rather fast to train, it can directly be applied to other sound domains, including an user's libraries with custom sound tags that could be mapped to specific generative controls. As a result, it fosters creativity and intuitive audio style experimentations.
Generalized Presentation Attack Detection: a face anti-spoofing evaluation proposal
Authors: Artur Costa-Pazo, David Jimenez-Cabello, Esteban Vazquez-Fernandez, Jose L. Alba-Castro, Roberto J. López-Sastre
Date: 2019-04-12 00:00:00
Tasks: Face Anti-Spoofing

Over the past few years, Presentation Attack Detection (PAD) has become a
fundamental part of facial recognition systems. Although much effort has been
devoted to anti-spoofing research, generalization in real scenarios remains a
challenge. In this paper we present a new open-source evaluation framework to
study the generalization capacity of face PAD methods, coined here as
face-GPAD. This framework facilitates the creation of new protocols focused on
the generalization problem establishing fair procedures of evaluation and
comparison between PAD solutions. We also introduce a large aggregated and
categorized dataset to address the problem of incompatibility between publicly
available datasets. Finally, we propose a benchmark adding two novel evaluation
protocols: one for measuring the effect introduced by the variations in face
resolution, and the second for evaluating the influence of adversarial
operating conditions.
Position-Aware Convolutional Networks for Traffic Prediction
Authors: Shiheng Ma, Jingcai Guo, Song Guo, Minyi Guo
Date: 2019-04-12 00:00:00
Tasks: Management, Position, Prediction, Traffic Prediction

Forecasting the future traffic flow distribution in an area is an important
issue for traffic management in an intelligent transportation system. The key
challenge of traffic prediction is to capture spatial and temporal relations
between future traffic flows and historical traffic due to highly dynamical
patterns of human activities. Most existing methods explore such relations by
fusing spatial and temporal features extracted from multi-source data. However,
they neglect position information which helps distinguish patterns on different
positions. In this paper, we propose a position-aware neural network that
integrates data features and position information. Our approach employs the
inception backbone network to capture rich features of traffic distribution on
the whole area. The novelty lies in that under the backbone network, we apply
position embedding technique used in neural language processing to represent
position information as embedding vectors which are learned during the
training. With these embedding vectors, we design position-aware convolution
which allows different kernels to process features of different positions.
Extensive experiments on two real-world datasets show that our approach
outperforms previous methods even with fewer data sources.
Generating Minimal Adversarial Perturbations with Integrated Adaptive Gradients
Authors: Yatie Xiao, Chi-Man Pun
Date: 2019-04-12 00:00:00
Tasks: Image Classification

Deep neural networks are easily fooled high confidence predictions for adversarial samples
An Empirical Evaluation Study on the Training of SDC Features for Dense Pixel Matching
Authors: René Schuster, Oliver Wasenmüller, Christian Unger, Didier Stricker
Date: 2019-04-12 00:00:00

Training a deep neural network is a non-trivial task. Not only the tuning of
hyperparameters, but also the gathering and selection of training data, the
design of the loss function, and the construction of training schedules is
important to get the most out of a model. In this study, we perform a set of
experiments all related to these issues. The model for which different training
strategies are investigated is the recently presented SDC descriptor network
(stacked dilated convolution). It is used to describe images on pixel-level for
dense matching tasks. Our work analyzes SDC in more detail, validates some best
practices for training deep neural networks, and provides insights into
training with multiple domain data.
Examining the Mapping Functions of Denoising Autoencoders in Singing Voice Separation
Authors: Stylianos Ioannis Mimilakis, Konstantinos Drossos, Estefanía Cano, Gerald Schuller
Date: 2019-04-12 00:00:00
Tasks: Decoder, Denoising, Knowledge Distillation, Music Source Separation

The goal of this work is to investigate what singing voice separation approaches based on neural networks learn from the data. We examine the mapping functions of neural networks based on the denoising autoencoder (DAE) model that are conditioned on the mixture magnitude spectra. To approximate the mapping functions, we propose an algorithm inspired by the knowledge distillation, denoted the neural couplings algorithm (NCA). The NCA yields a matrix that expresses the mapping of the mixture to the target source magnitude information. Using the NCA, we examine the mapping functions of three fundamental DAE-based models in music source separation; one with single-layer encoder and decoder, one with multi-layer encoder and single-layer decoder, and one using skip-filtering connections (SF) with a single-layer encoding and decoding. We first train these models with realistic data to estimate the singing voice magnitude spectra from the corresponding mixture. We then use the optimized models and test spectral data as input to the NCA. Our experimental findings show that approaches based on the DAE model learn scalar filtering operators, exhibiting a predominant diagonal structure in their corresponding mapping functions, limiting the exploitation of inter-frequency structure of music data. In contrast, skip-filtering connections are shown to assist the DAE model in learning filtering operators that exploit richer inter-frequency structures.
Interpretable Classification from Skin Cancer Histology Slides Using Deep Learning: A Retrospective Multicenter Study
Authors: Peizhen Xie, Ke Zuo, Yu Zhang, Fangfang Li, Mingzhu Yin, Kai Lu
Date: 2019-04-12 00:00:00
Tasks: Diversity, General Classification, whole slide images

For diagnosing melanoma, hematoxylin and eosin (H&E) stained tissue slides
remains the gold standard. These images contain quantitative information in
different magnifications. In the present study, we investigated whether deep
convolutional neural networks can extract structural features of complex
tissues directly from these massive size images in a patched way. In order to
face the challenge arise from morphological diversity in histopathological
slides, we built a multicenter database of 2241 digital whole-slide images from
1321 patients from 2008 to 2018. We trained both ResNet50 and Vgg19 using over
9.95 million patches by transferring learning, and test performance with two
kinds of critical classifications: malignant melanomas versus benign nevi in
separate and mixed magnification; and distinguish among nevi in maximum
magnification. The CNNs achieves superior performance across both tasks,
demonstrating an AI capable of classifying skin cancer in the analysis from
histopathological images. For making the classifications reasonable, the
visualization of CNN representations is furthermore used to identify cells
between melanoma and nevi. Regions of interest (ROI) are also located which are
significantly helpful, giving pathologists more support of correctly diagnosis.
Geometry-Aware Maximum Likelihood Estimation of Intrinsic Dimension
Authors: Marina Gomtsyan, Nikita Mokrov, Maxim Panov, Yury Yanovich
Date: 2019-04-12 00:00:00
Tasks: regression

The existing approaches to intrinsic dimension estimation usually are not
reliable when the data are nonlinearly embedded in the high dimensional space.
In this work, we show that the explicit accounting to geometric properties of
unknown support leads to the polynomial correction to the standard maximum
likelihood estimate of intrinsic dimension for flat manifolds. The proposed
algorithm (GeoMLE) realizes the correction by regression of standard MLEs based
on distances to nearest neighbors for different sizes of neighborhoods.
Moreover, the proposed approach also efficiently handles the case of nonuniform
sampling of the manifold. We perform numerous experiments on different
synthetic and real-world datasets. The results show that our algorithm achieves
state-of-the-art performance, while also being computationally efficient and
robust to noise in the data.
Towards Photographic Image Manipulation with Balanced Growing of Generative Autoencoders
Authors: Ari Heljakka, Arno Solin, Juho Kannala
Date: 2019-04-12 00:00:00
Tasks: Attribute, Disentanglement, Image Manipulation

We present a generative autoencoder that provides fast encoding, faithful reconstructions (eg. retaining the identity of a face), sharp generated/reconstructed samples in high resolutions, and a well-structured latent space that supports semantic manipulation of the inputs. There are no current autoencoder or GAN models that satisfactorily achieve all of these. We build on the progressively growing autoencoder model PIONEER, for which we completely alter the training dynamics based on a careful analysis of recently introduced normalization schemes. We show significantly improved visual and quantitative results for face identity conservation in CelebAHQ. Our model achieves state-of-the-art disentanglement of latent space, both quantitatively and via realistic image attribute manipulations. On the LSUN Bedrooms dataset, we improve the disentanglement performance of the vanilla PIONEER, despite having a simpler model. Overall, our results indicate that the PIONEER networks provide a way towards photorealistic face manipulation.
A streaming feature-based compression method for data from instrumented infrastructure
Authors: Alastair Gregory, Din-Houn Lau, Alex Tessier, Pan Zhang
Date: 2019-04-12 00:00:00

An increasing amount of civil engineering applications are utilising data
acquired from infrastructure instrumented with sensing devices. This data has
an important role in monitoring the response of these structures to excitation,
and evaluating structural health. In this paper we seek to monitor
pedestrian-events (such as a person walking) on a footbridge using strain and
acceleration data. The rate of this data acquisition and the number of sensing
devices make the storage and analysis of this data a computational challenge.
We introduce a streaming method to compress the sensor data, whilst preserving
key patterns and features (unique to different sensor types) corresponding to
pedestrian-events. Numerical demonstrations of the methodology on data obtained
from strain sensors and accelerometers on the pedestrian footbridge are
provided to show the trade-off between compression and accuracy during and
in-between periods of pedestrian-events.
PWOC-3D: Deep Occlusion-Aware End-to-End Scene Flow Estimation
Authors: Rohan Saxena, René Schuster, Oliver Wasenmüller, Didier Stricker
Date: 2019-04-12 00:00:00
Tasks: Optical Flow Estimation, Scene Flow Estimation, Stereo Matching, Stereo Matching Hand

In the last few years, convolutional neural networks (CNNs) have demonstrated
increasing success at learning many computer vision tasks including dense
estimation problems such as optical flow and stereo matching. However, the
joint prediction of these tasks, called scene flow, has traditionally been
tackled using slow classical methods based on primitive assumptions which fail
to generalize. The work presented in this paper overcomes these drawbacks
efficiently (in terms of speed and accuracy) by proposing PWOC-3D, a compact
CNN architecture to predict scene flow from stereo image sequences in an
end-to-end supervised setting. Further, large motion and occlusions are
well-known problems in scene flow estimation. PWOC-3D employs specialized
design decisions to explicitly model these challenges. In this regard, we
propose a novel self-supervised strategy to predict occlusions from images
(learned without any labeled occlusion data). Leveraging several such
constructs, our network achieves competitive results on the KITTI benchmark and
the challenging FlyingThings3D dataset. Especially on KITTI, PWOC-3D achieves
the second place among end-to-end deep learning methods with 48 times fewer
parameters than the top-performing method.
Evolved Art with Transparent, Overlapping, and Geometric Shapes
Authors: Joachim Berg, Nils Gustav Andreas Berggren, Sivert Allergodt Borgeteien, Christian Ruben Alexander Jahren, Arqam Sajid, Stefano Nichele
Date: 2019-04-12 00:00:00

In this work, an evolutionary art project is presented where images are approximated by transparent, overlapping and geometric shapes of different types, e.g., polygons, circles, lines. Genotypes representing features and order of the geometric shapes are evolved with a fitness function that has the corresponding pixels of an input image as a target goal. A genotype-to-phenotype mapping is therefore applied to render images, as the chosen genetic representation is indirect, i.e., genotypes do not include pixels but a combination of shapes with their properties. Different combinations of shapes, quantity of shapes, mutation types and populations are tested. The goal of the work herein is twofold: (1) to approximate images as precisely as possible with evolved indirect encodings, (2) to produce visually appealing results and novel artistic styles.
Face De-occlusion using 3D Morphable Model and Generative Adversarial Network
Authors: Xiaowei Yuan, In Kyu Park
Date: 2019-04-12 00:00:00
Proceeding: ICCV 2019 10
Tasks: 3D Face Reconstruction, Face Model, Face Reconstruction, Generative Adversarial Network

In recent decades, 3D morphable model (3DMM) has been commonly used in image-based photorealistic 3D face reconstruction. However, face images are often corrupted by serious occlusion by non-face objects including eyeglasses, masks, and hands. Such objects block the correct capture of landmarks and shading information. Therefore, the reconstructed 3D face model is hardly reusable. In this paper, a novel method is proposed to restore de-occluded face images based on inverse use of 3DMM and generative adversarial network. We utilize the 3DMM prior to the proposed adversarial network and combine a global and local adversarial convolutional neural network to learn face de-occlusion model. The 3DMM serves not only as geometric prior but also proposes the face region for the local discriminator. Experiment results confirm the effectiveness and robustness of the proposed algorithm in removing challenging types of occlusions with various head poses and illumination. Furthermore, the proposed method reconstructs the correct 3D face model with de-occluded textures.
A Crowdsourced Frame Disambiguation Corpus with Ambiguity
Authors: Anca Dumitrache, Lora Aroyo, Chris Welty
Date: 2019-04-12 00:00:00
Proceeding: NAACL 2019 6
Tasks: LEMMA, Sentence

We present a resource for the task of FrameNet semantic frame disambiguation
of over 5,000 word-sentence pairs from the Wikipedia corpus. The annotations
were collected using a novel crowdsourcing approach with multiple workers per
sentence to capture inter-annotator disagreement. In contrast to the typical
approach of attributing the best single frame to each word, we provide a list
of frames with disagreement-based scores that express the confidence with which
each frame applies to the word. This is based on the idea that inter-annotator
disagreement is at least partly caused by ambiguity that is inherent to the
text and frames. We have found many examples where the semantics of individual
frames overlap sufficiently to make them acceptable alternatives for
interpreting a sentence. We have argued that ignoring this ambiguity creates an
overly arbitrary target for training and evaluating natural language processing
systems - if humans cannot agree, why would we expect the correct answer from a
machine to be any different? To process this data we also utilized an expanded
lemma-set provided by the Framester system, which merges FN with WordNet to
enhance coverage. Our dataset includes annotations of 1,000 sentence-word pairs
whose lemmas are not part of FN. Finally we present metrics for evaluating
frame disambiguation systems that account for ambiguity.
Adapting Sequence to Sequence models for Text Normalization in Social Media
Authors: Ismini Lourentzou, Kabir Manghnani, ChengXiang Zhai
Date: 2019-04-12 00:00:00
Tasks: Decoder, Lexical Normalization, Text Normalization

Social media offer an abundant source of valuable raw data, however informal
writing can quickly become a bottleneck for many natural language processing
(NLP) tasks. Off-the-shelf tools are usually trained on formal text and cannot
explicitly handle noise found in short online posts. Moreover, the variety of
frequently occurring linguistic variations presents several challenges, even
for humans who might not be able to comprehend the meaning of such posts,
especially when they contain slang and abbreviations. Text Normalization aims
to transform online user-generated text to a canonical form. Current text
normalization systems rely on string or phonetic similarity and classification
models that work on a local fashion. We argue that processing contextual
information is crucial for this task and introduce a social media text
normalization hybrid word-character attention-based encoder-decoder model that
can serve as a pre-processing step for NLP applications to adapt to noisy text
in social media. Our character-based component is trained on synthetic
adversarial examples that are designed to capture errors commonly found in
online user-generated text. Experiments show that our model surpasses neural
architectures designed for text normalization and achieves comparable
performance with state-of-the-art related work.
Evaluating Robustness of Deep Image Super-Resolution against Adversarial Attacks
Authors: Jun-Ho Choi, huan zhang, Jun-Hyuk Kim, Cho-Jui Hsieh, Jong-Seok Lee
Date: 2019-04-12 00:00:00
Proceeding: ICCV 2019 10
Tasks: Image Super-Resolution, Super-Resolution

Single-image super-resolution aims to generate a high-resolution version of a low-resolution image, which serves as an essential component in many computer vision applications. This paper investigates the robustness of deep learning-based super-resolution methods against adversarial attacks, which can significantly deteriorate the super-resolved images without noticeable distortion in the attacked low-resolution images. It is demonstrated that state-of-the-art deep super-resolution methods are highly vulnerable to adversarial attacks. Different levels of robustness of different methods are analyzed theoretically and experimentally. We also present analysis on transferability of attacks, and feasibility of targeted attacks and universal attacks.
STC Speaker Recognition Systems for the VOiCES From a Distance Challenge
Authors: Sergey Novoselov, Aleksei Gusev, Artem Ivanov, Timur Pekhovsky, Andrey Shulipa, Galina Lavrentyeva, Vladimir Volokhov, Alexandr Kozlov
Date: 2019-04-12 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Data Augmentation, Metric Learning, Room Impulse Response (RIR), Speaker Recognition, speech-recognition, Speech Recognition

This paper presents the Speech Technology Center (STC) speaker recognition
(SR) systems submitted to the VOiCES From a Distance challenge 2019. The
challenge's SR task is focused on the problem of speaker recognition in single
channel distant/far-field audio under noisy conditions. In this work we
investigate different deep neural networks architectures for speaker embedding
extraction to solve the task. We show that deep networks with residual frame
level connections outperform more shallow architectures. Simple energy based
speech activity detector (SAD) and automatic speech recognition (ASR) based SAD
are investigated in this work. We also address the problem of data preparation
for robust embedding extractors training. The reverberation for the data
augmentation was performed using automatic room impulse response generator. In
our systems we used discriminatively trained cosine similarity metric learning
model as embedding backend. Scores normalization procedure was applied for each
individual subsystem we used. Our final submitted systems were based on the
fusion of different subsystems. The results obtained on the VOiCES development
and evaluation sets demonstrate effectiveness and robustness of the proposed
systems when dealing with distant/far-field audio under noisy conditions.