pwc-archive/papers-with-abstracts huggingface dataset:
Feedback GAN (FBGAN) for DNA: a Novel Feedback-Loop Architecture for Optimizing Protein Functions
Authors: Anvita Gupta, James Zou
Date: 2018-04-05 00:00:00

Generative Adversarial Networks (GANs) represent an attractive and novel
approach to generate realistic data, such as genes, proteins, or drugs, in
synthetic biology. Here, we apply GANs to generate synthetic DNA sequences
encoding for proteins of variable length. We propose a novel feedback-loop
architecture, called Feedback GAN (FBGAN), to optimize the synthetic gene
sequences for desired properties using an external function analyzer. The
proposed architecture also has the advantage that the analyzer need not be
differentiable. We apply the feedback-loop mechanism to two examples: 1)
generating synthetic genes coding for antimicrobial peptides, and 2) optimizing
synthetic genes for the secondary structure of their resulting peptides. A
suite of metrics demonstrate that the GAN generated proteins have desirable
biophysical properties. The FBGAN architecture can also be used to optimize
GAN-generated datapoints for useful properties in domains beyond genomics.
Using a Classifier Ensemble for Proactive Quality Monitoring and Control: the impact of the choice of classifiers types, selection criterion, and fusion process
Authors: Philippe Thomas, Hind Bril El Haouzi, Marie-Christine Suhner, André Thomas, Emmanuel Zimmermann, Mélanie Noyel
Date: 2018-04-05 00:00:00
Tasks: Classification, General Classification, Management

In recent times, the manufacturing processes are faced with many external or
internal (the increase of customized product rescheduling , process
reliability,..) changes. Therefore, monitoring and quality management
activities for these manufacturing processes are difficult. Thus, the managers
need more proactive approaches to deal with this variability. In this study, a
proactive quality monitoring and control approach based on classifiers to
predict defect occurrences and provide optimal values for factors critical to
the quality processes is proposed. In a previous work (Noyel et al. 2013), the
classification approach had been used in order to improve the quality of a
lacquering process at a company plant; the results obtained are promising, but
the accuracy of the classification model used needs to be improved. One way to
achieve this is to construct a committee of classifiers (referred to as an
ensemble) to obtain a better predictive model than its constituent models.
However, the selection of the best classification methods and the construction
of the final ensemble still poses a challenging issue. In this study, we focus
and analyze the impact of the choice of classifier types on the accuracy of the
classifier ensemble; in addition, we explore the effects of the selection
criterion and fusion process on the ensemble accuracy as well. Several fusion
scenarios were tested and compared based on a real-world case. Our results show
that using an ensemble classification leads to an increase in the accuracy of
the classifier models. Consequently, the monitoring and control of the
considered real-world case can be improved.
Hallucinated-IQA: No-Reference Image Quality Assessment via Adversarial Learning
Authors: Kwan-Yee Lin, Guanxiang Wang
Date: 2018-04-05 00:00:00
Proceeding: CVPR 2018 6
Tasks: Hallucination, Image Quality Assessment, No-Reference Image Quality Assessment, NR-IQA

No-reference image quality assessment (NR-IQA) is a fundamental yet
challenging task in low-level computer vision community. The difficulty is
particularly pronounced for the limited information, for which the
corresponding reference for comparison is typically absent. Although various
feature extraction mechanisms have been leveraged from natural scene statistics
to deep neural networks in previous methods, the performance bottleneck still
exists. In this work, we propose a hallucination-guided quality regression
network to address the issue. We firstly generate a hallucinated reference
constrained on the distorted image, to compensate the absence of the true
reference. Then, we pair the information of hallucinated reference with the
distorted image, and forward them to the regressor to learn the perceptual
discrepancy with the guidance of an implicit ranking relationship within the
generator, and therefore produce the precise quality prediction. To demonstrate
the effectiveness of our approach, comprehensive experiments are evaluated on
four popular image quality assessment benchmarks. Our method significantly
outperforms all the previous state-of-the-art methods by large margins. The
code and model will be publicly available on the project page
https://kwanyeelin.github.io/projects/HIQA/HIQA.html.
Semi-Supervised Classification for oil reservoir
Authors: Yanan Li, Haixiang Guo, Andrew P Paplinski
Date: 2018-04-05 00:00:00
Tasks: Classification, General Classification

This paper addresses the general problem of accurate identification of oil
reservoirs. Recent improvements in well or borehole logging technology have
resulted in an explosive amount of data available for processing. The
traditional methods of analysis of the logs characteristics by experts require
significant amount of time and money and is no longer practicable. In this
paper, we use the semi-supervised learning to solve the problem of
ever-increasing amount of unlabelled data available for interpretation. The
experts are needed to label only a small amount of the log data. The neural
network classifier is first trained with the initial labelled data. Next,
batches of unlabelled data are being classified and the samples with the very
high class probabilities are being used in the next training session,
bootstrapping the classifier. The process of training, classifying, enhancing
the labelled data is repeated iteratively until the stopping criteria are met,
that is, no more high probability samples are found. We make an empirical study
on the well data from Jianghan oil field and test the performance of the neural
network semi-supervised classifier. We compare this method with other
classifiers. The comparison results show that our neural network
semi-supervised classifier is superior to other classification methods.
Dockerface: an Easy to Install and Use Faster R-CNN Face Detector in a Docker Container
Authors: Nataniel Ruiz, James M. Rehg
Date: 2017-08-15 00:00:00
Tasks: Face Detection, Face Recognition, Facial Landmark Detection, Pose Estimation, Sentiment Analysis

Face detection is a very important task and a necessary pre-processing step
for many applications such as facial landmark detection, pose estimation,
sentiment analysis and face recognition. Not only is face detection an
important pre-processing step in computer vision applications but also in
computational psychology, behavioral imaging and other fields where researchers
might not be initiated in computer vision frameworks and state-of-the-art
detection applications. A large part of existing research that includes face
detection as a pre-processing step uses existing out-of-the-box detectors such
as the HoG-based dlib and the OpenCV Haar face detector which are no longer
state-of-the-art - they are primarily used because of their ease of use and
accessibility. We introduce Dockerface, a very accurate Faster R-CNN face
detector in a Docker container which requires no training and is easy to
install and use.
Cancelable Indexing Based on Low-rank Approximation of Correlation-invariant Random Filtering for Fast and Secure Biometric Identification
Authors: Takao Murakami, Tetsushi Ohki, Yosuke Kaga, Masakazu Fujio, Kenta Takahashi
Date: 2018-04-05 00:00:00

A cancelable biometric scheme called correlation-invariant random filtering
(CIRF) is known as a promising template protection scheme. This scheme
transforms a biometric feature represented as an image via the 2D number
theoretic transform (NTT) and random filtering. CIRF has perfect secrecy in
that the transformed feature leaks no information about the original feature.
However, CIRF cannot be applied to large-scale biometric identification, since
the 2D inverse NTT in the matching phase requires high computational time.
Furthermore, existing biometric indexing schemes cannot be used in conjunction
with template protection schemes to speed up biometric identification, since a
biometric index leaks some information about the original feature. In this
paper, we propose a novel indexing scheme called "cancelable indexing" to speed
up CIRF without losing its security properties. The proposed scheme is based on
fast computation of CIRF via low-rank approximation of biometric images and via
a minimum spanning tree representation of low-rank matrices in the Fourier
domain. We prove that the transformed index leaks no information about the
original index and the original biometric feature (i.e., perfect secrecy), and
thoroughly discuss the security of the proposed scheme. We also demonstrate
that it significantly reduces the one-to-many matching time using a finger-vein
dataset that includes six fingers from 505 subjects.
Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning
Authors: Xuan Wang, Yu Zhang, Xiang Ren, Yuhao Zhang, Marinka Zitnik, Jingbo Shang, Curtis Langlotz, Jiawei Han
Date: 2018-01-30 00:00:00
Tasks: Feature Engineering, Multi-Task Learning, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Vocal Bursts Type Prediction

Motivation: State-of-the-art biomedical named entity recognition (BioNER)
systems often require handcrafted features specific to each entity type, such
as genes, chemicals and diseases. Although recent studies explored using neural
network models for BioNER to free experts from manual feature engineering, the
performance remains limited by the available training data for each entity
type. Results: We propose a multi-task learning framework for BioNER to
collectively use the training data of different types of entities and improve
the performance on each of them. In experiments on 15 benchmark BioNER
datasets, our multi-task model achieves substantially better performance
compared with state-of-the-art BioNER systems and baseline neural sequence
labeling models. Further analysis shows that the large performance gains come
from sharing character- and word-level information among relevant biomedical
entities across differently labeled corpora.
Learning to Separate Object Sounds by Watching Unlabeled Video
Authors: Ruohan Gao, Rogerio Feris, Kristen Grauman
Date: 2018-04-05 00:00:00
Proceeding: ECCV 2018 9
Tasks: Audio Denoising, Audio Source Separation, Denoising, Multi-Label Learning

Perceiving a scene most fully requires all the senses. Yet modeling how
objects look and sound is challenging: most natural scenes and events contain
multiple objects, and the audio track mixes all the sound sources together. We
propose to learn audio-visual object models from unlabeled video, then exploit
the visual context to perform audio source separation in novel videos. Our
approach relies on a deep multi-instance multi-label learning framework to
disentangle the audio frequency bases that map to individual visual objects,
even without observing/hearing those objects in isolation. We show how the
recovered disentangled bases can be used to guide audio source separation to
obtain better-separated, object-level sounds. Our work is the first to learn
audio source separation from large-scale "in the wild" videos containing
multiple audio sources per video. We obtain state-of-the-art results on
visually-aided audio source separation and audio denoising. Our video results:
http://vision.cs.utexas.edu/projects/separating_object_sounds/
The structure of evolved representations across different substrates for artificial intelligence
Authors: Arend Hintze, Douglas Kirkpatrick, Christoph Adami
Date: 2018-04-05 00:00:00
Tasks: General Classification

Artificial neural networks (ANNs), while exceptionally useful for
classification, are vulnerable to misdirection. Small amounts of noise can
significantly affect their ability to correctly complete a task. Instead of
generalizing concepts, ANNs seem to focus on surface statistical regularities
in a given task. Here we compare how recurrent artificial neural networks, long
short-term memory units, and Markov Brains sense and remember their
environments. We show that information in Markov Brains is localized and
sparsely distributed, while the other neural network substrates "smear"
information about the environment across all nodes, which makes them vulnerable
to noise.
Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images
Authors: Nanyang Wang, yinda zhang, Zhuwen Li, Yanwei Fu, Wei Liu, Yu-Gang Jiang
Date: 2018-04-05 00:00:00
Proceeding: ECCV 2018 9
Tasks: 3D geometry, 3D Object Reconstruction

We propose an end-to-end deep learning architecture that produces a 3D shape
in triangular mesh from a single color image. Limited by the nature of deep
neural network, previous methods usually represent a 3D shape in volume or
point cloud, and it is non-trivial to convert them to the more ready-to-use
mesh model. Unlike the existing methods, our network represents 3D mesh in a
graph-based convolutional neural network and produces correct geometry by
progressively deforming an ellipsoid, leveraging perceptual features extracted
from the input image. We adopt a coarse-to-fine strategy to make the whole
deformation procedure stable, and define various of mesh related losses to
capture properties of different levels to guarantee visually appealing and
physically accurate 3D geometry. Extensive experiments show that our method not
only qualitatively produces mesh model with better details, but also achieves
higher 3D shape estimation accuracy compared to the state-of-the-art.
Review of Deep Learning
Authors: Rong Zhang, Weiping Li, Tong Mo
Date: 2018-04-05 00:00:00
Tasks: Deep Learning

In recent years, China, the United States and other countries, Google and
other high-tech companies have increased investment in artificial intelligence.
Deep learning is one of the current artificial intelligence research's key
areas. This paper analyzes and summarizes the latest progress and future
research directions of deep learning. Firstly, three basic models of deep
learning are outlined, including multilayer perceptrons, convolutional neural
networks, and recurrent neural networks. On this basis, we further analyze the
emerging new models of convolution neural networks and recurrent neural
networks. This paper then summarizes deep learning's applications in many areas
of artificial intelligence, including speech processing, computer vision,
natural language processing and so on. Finally, this paper discusses the
existing problems of deep learning and gives the corresponding possible
solutions.
Jointly Detecting and Separating Singing Voice: A Multi-Task Approach
Authors: Daniel Stoller, Sebastian Ewert, Simon Dixon
Date: 2018-04-05 00:00:00
Tasks: Action Detection, Activity Detection, Multi-Task Learning

A main challenge in applying deep learning to music processing is the
availability of training data. One potential solution is Multi-task Learning,
in which the model also learns to solve related auxiliary tasks on additional
datasets to exploit their correlation. While intuitive in principle, it can be
challenging to identify related tasks and construct the model to optimally
share information between tasks. In this paper, we explore vocal activity
detection as an additional task to stabilise and improve the performance of
vocal separation. Further, we identify problematic biases specific to each
dataset that could limit the generalisation capability of separation and
detection models, to which our proposed approach is robust. Experiments show
improved performance in separation as well as vocal detection compared to
single-task baselines. However, we find that the commonly used
Signal-to-Distortion Ratio (SDR) metrics did not capture the improvement on
non-vocal sections, indicating the need for improved evaluation methodologies.
A Pyramid CNN for Dense-Leaves Segmentation
Authors: Daniel D. Morris
Date: 2018-04-05 00:00:00
Tasks: Segmentation

Automatic detection and segmentation of overlapping leaves in dense foliage
can be a difficult task, particularly for leaves with strong textures and high
occlusions. We present Dense-Leaves, an image dataset with ground truth
segmentation labels that can be used to train and quantify algorithms for leaf
segmentation in the wild. We also propose a pyramid convolutional neural
network with multi-scale predictions that detects and discriminates leaf
boundaries from interior textures. Using these detected boundaries,
closed-contour boundaries around individual leaves are estimated with a
watershed-based algorithm. The result is an instance segmenter for dense
leaves. Promising segmentation results for leaves in dense foliage are
obtained.
Multi-view Registration Based on Weighted Low Rank and Sparse Matrix Decomposition of Motions
Authors: Congcong Jin, Jihua Zhu, Yaochen Li, Shanmin Pang, Lei Chen, Jun Wang
Date: 2017-09-25 00:00:00

Recently, the low rank and sparse (LRS) matrix decomposition has been
introduced as an effective mean to solve the multi-view registration. It views
each available relative motion as a block element to reconstruct one matrix so
as to approximate the low rank matrix, where global motions can be recovered
for multi-view registration. However, this approach is sensitive to the
sparsity of the reconstructed matrix and it treats all block elements equally
in spite of their varied reliability. Therefore, this paper proposes an
effective approach for multi-view registration by the weighted LRS
decomposition. Based on the anti-symmetry property of relative motions, it
firstly proposes a completion strategy to reduce the sparsity of the
reconstructed matrix. The reduced sparsity of reconstructed matrix can improve
the robustness of LRS decomposition. Then, it proposes the weighted LRS
decomposition, where each block element is assigned with one estimated weight
to denote its reliability. By introducing the weight, more accurate
registration results can be recovered from the estimated low rank matrix with
good efficiency. Experimental results tested on public data sets illustrate the
superiority of the proposed approach over the state-of-the-art approaches on
robustness, accuracy, and efficiency.
Learning with Correntropy-induced Losses for Regression with Mixture of Symmetric Stable Noise
Authors: Yunlong Feng, Yiming Ying
Date: 2018-03-01 00:00:00
Tasks: regression

In recent years, correntropy and its applications in machine learning have been drawing continuous attention owing to its merits in dealing with non-Gaussian noise and outliers. However, theoretical understanding of correntropy, especially in the statistical learning context, is still limited. In this study, within the statistical learning framework, we investigate correntropy based regression in the presence of non-Gaussian noise or outliers. Motivated by the practical way of generating non-Gaussian noise or outliers, we introduce mixture of symmetric stable noise, which include Gaussian noise, Cauchy noise, and their mixture as special cases, to model non-Gaussian noise or outliers. We demonstrate that under the mixture of symmetric stable noise assumption, correntropy based regression can learn the conditional mean function or the conditional median function well without resorting to the finite-variance or even the finite first-order moment condition on the noise. In particular, for the above two cases, we establish asymptotic optimal learning rates for correntropy based regression estimators that are asymptotically of type $\mathcal{O}(n^{-1})$. These results justify the effectiveness of the correntropy based regression estimators in dealing with outliers as well as non-Gaussian noise. We believe that the present study completes our understanding towards correntropy based regression from a statistical learning viewpoint, and may also shed some light on robust statistical learning for regression.
Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction
Authors: Huangying Zhan, Ravi Garg, Chamara Saroj Weerasekera, Kejie Li, Harsh Agarwal, Ian Reid
Date: 2018-03-11 00:00:00
Proceeding: CVPR 2018 6
Tasks: Depth And Camera Motion, Depth Estimation, Depth Prediction, Monocular Depth Estimation, Visual Odometry

Despite learning based methods showing promising results in single view depth
estimation and visual odometry, most existing approaches treat the tasks in a
supervised manner. Recent approaches to single view depth estimation explore
the possibility of learning without full supervision via minimizing photometric
error. In this paper, we explore the use of stereo sequences for learning depth
and visual odometry. The use of stereo sequences enables the use of both
spatial (between left-right pairs) and temporal (forward backward) photometric
warp error, and constrains the scene depth and camera motion to be in a common,
real-world scale. At test time our framework is able to estimate single view
depth and two-view odometry from a monocular sequence. We also show how we can
improve on a standard photometric warp loss by considering a warp of deep
features. We show through extensive experiments that: (i) jointly training for
single view depth and visual odometry improves depth prediction because of the
additional constraint imposed on depths and achieves competitive results for
visual odometry; (ii) deep feature-based warping loss improves upon simple
photometric warp loss for both single view depth estimation and visual
odometry. Our method outperforms existing learning based methods on the KITTI
driving dataset in both tasks. The source code is available at
https://github.com/Huangying-Zhan/Depth-VO-Feat
Hypertree Decompositions Revisited for PGMs
Authors: Aarthy Shivram Arun, Sai Vikneshwar Mani Jayaraman, Christopher Ré, Atri Rudra
Date: 2018-04-05 00:00:00

We revisit the classical problem of exact inference on probabilistic
graphical models (PGMs). Our algorithm is based on recent worst-case optimal
database join algorithms, which can be asymptotically faster than traditional
data processing methods. We present the first empirical evaluation of these new
algorithms via JoinInfer, a new exact inference engine. We empirically explore
the properties of the data for which our engine can be expected to outperform
traditional inference engines refining current theoretical notions. Further,
JoinInfer outperforms existing state-of-the-art inference engines (ACE, IJGP
and libDAI) on some standard benchmark datasets by up to a factor of 630x.
Finally, we propose a promising data-driven heuristic that extends JoinInfer to
automatically tailor its parameters and/or switch to the traditional inference
algorithms.
Unifying Bilateral Filtering and Adversarial Training for Robust Neural Networks
Authors: Neale Ratzlaff, Li Fuxin
Date: 2018-04-05 00:00:00

Recent analysis of deep neural networks has revealed their vulnerability to
carefully structured adversarial examples. Many effective algorithms exist to
craft these adversarial examples, but performant defenses seem to be far away.
In this work, we explore the use of edge-aware bilateral filtering as a
projection back to the space of natural images. We show that bilateral
filtering is an effective defense in multiple attack settings, where the
strength of the adversary gradually increases. In the case of an adversary who
has no knowledge of the defense, bilateral filtering can remove more than 90%
of adversarial examples from a variety of different attacks. To evaluate
against an adversary with complete knowledge of our defense, we adapt the
bilateral filter as a trainable layer in a neural network and show that adding
this layer makes ImageNet images significantly more robust to attacks. When
trained under a framework of adversarial training, we show that the resulting
model is hard to fool with even the best attack methods.