pwc-archive/papers-with-abstracts huggingface dataset:
On the Behavior of Convolutional Nets for Feature Extraction
Authors: Dario Garcia-Gasulla, Ferran Parés, Armand Vilalta, Jonatan Moreno, Eduard Ayguadé, Jesús Labarta, Ulises Cortés, Toyotaro Suzumura
Date: 2017-03-03 00:00:00
Tasks: Descriptive, Representation Learning, Transfer Learning

Deep neural networks are representation learning techniques. During training,
a deep net is capable of generating a descriptive language of unprecedented
size and detail in machine learning. Extracting the descriptive language coded
within a trained CNN model (in the case of image data), and reusing it for
other purposes is a field of interest, as it provides access to the visual
descriptors previously learnt by the CNN after processing millions of images,
without requiring an expensive training phase. Contributions to this field
(commonly known as feature representation transfer or transfer learning) have
been purely empirical so far, extracting all CNN features from a single layer
close to the output and testing their performance by feeding them to a
classifier. This approach has provided consistent results, although its
relevance is limited to classification tasks. In a completely different
approach, in this paper we statistically measure the discriminative power of
every single feature found within a deep CNN, when used for characterizing
every class of 11 datasets. We seek to provide new insights into the behavior
of CNN features, particularly the ones from convolutional layers, as this can
be relevant for their application to knowledge representation and reasoning.
Our results confirm that low and middle level features may behave differently
to high level features, but only under certain conditions. We find that all CNN
features can be used for knowledge representation purposes both by their
presence or by their absence, doubling the information a single CNN feature may
provide. We also study how much noise these features may include, and propose a
thresholding approach to discard most of it. All these insights have a direct
application to the generation of CNN embedding spaces.
TernaryNet: Faster Deep Model Inference without GPUs for Medical 3D Segmentation using Sparse and Binary Convolutions
Authors: Mattias P. Heinrich, Max Blendowski, Ozan Oktay
Date: 2018-01-29 00:00:00
Tasks: 3D Medical Imaging Segmentation, Diagnostic, GPU, Open-Ended Question Answering, Pancreas Segmentation, Retrieval

Deep convolutional neural networks (DCNN) are currently ubiquitous in medical
imaging. While their versatility and high quality results for common image
analysis tasks including segmentation, localisation and prediction is
astonishing, the large representational power comes at the cost of highly
demanding computational effort. This limits their practical applications for
image guided interventions and diagnostic (point-of-care) support using mobile
devices without graphics processing units (GPU). We propose a new scheme that
approximates both trainable weights and neural activations in deep networks by
ternary values and tackles the open question of backpropagation when dealing
with non-differentiable functions. Our solution enables the removal of the
expensive floating-point matrix multiplications throughout any convolutional
neural network and replaces them by energy and time preserving binary operators
and population counts. Our approach, which is demonstrated using a
fully-convolutional network (FCN) for CT pancreas segmentation leads to more
than 10-fold reduced memory requirements and we provide a concept for
sub-second inference without GPUs. Our ternary approximation obtains high
accuracies (without any post-processing) with a Dice overlap of 71.0% that are
statistically equivalent to using networks with high-precision weights and
activations. We further demonstrate the significant improvements reached in
comparison to binary quantisation and without our proposed ternary hyperbolic
tangent continuation. We present a key enabling technique for highly efficient
DCNN inference without GPUs that will help to bring the advances of deep
learning to practical clinical applications. It has also great promise for
improving accuracies in large-scale medical data retrieval.
Local Visual Microphones: Improved Sound Extraction from Silent Video
Authors: Mohammad Amin Shabani, Laleh Samadfam, Mohammad Amin Sadeghi
Date: 2018-01-29 00:00:00

Sound waves cause small vibrations in nearby objects. A few techniques exist
in the literature that can extract sound from video. In this paper we study
local vibration patterns at different image locations. We show that different
locations in the image vibrate differently. We carefully aggregate local
vibrations and produce a sound quality that improves state-of-the-art. We show
that local vibrations could have a time delay because sound waves take time to
travel through the air. We use this phenomenon to estimate sound direction. We
also present a novel algorithm that speeds up sound extraction by two to three
orders of magnitude and reaches real-time performance in a 20KHz video.
Classification of sparsely labeled spatio-temporal data through semi-supervised adversarial learning
Authors: Atanas Mirchev, Seyed-Ahmad Ahmadi
Date: 2018-01-26 00:00:00
Tasks: General Classification, Retrieval

In recent years, Generative Adversarial Networks (GAN) have emerged as a
powerful method for learning the mapping from noisy latent spaces to realistic
data samples in high-dimensional space. So far, the development and application
of GANs have been predominantly focused on spatial data such as images. In this
project, we aim at modeling of spatio-temporal sensor data instead, i.e.
dynamic data over time. The main goal is to encode temporal data into a global
and low-dimensional latent vector that captures the dynamics of the
spatio-temporal signal. To this end, we incorporate auto-regressive RNNs,
Wasserstein GAN loss, spectral norm weight constraints and a semi-supervised
learning scheme into InfoGAN, a method for retrieval of meaningful latents in
adversarial learning. To demonstrate the modeling capability of our method, we
encode full-body skeletal human motion from a large dataset representing 60
classes of daily activities, recorded in a multi-Kinect setup. Initial results
indicate competitive classification performance of the learned latent
representations, compared to direct CNN/RNN inference. In future work, we plan
to apply this method on a related problem in the medical domain, i.e. on
recovery of meaningful latents in gait analysis of patients with vertigo and
balance disorders.
A Gaussian Process Regression Model for Distribution Inputs
Authors: François Bachoc, Fabrice Gamboa, Jean-Michel Loubes, Nil Venet
Date: 2017-01-31 00:00:00
Tasks: BIG-bench Machine Learning, Gaussian Processes, model, regression

Monge-Kantorovich distances, otherwise known as Wasserstein distances, have
received a growing attention in statistics and machine learning as a powerful
discrepancy measure for probability distributions. In this paper, we focus on
forecasting a Gaussian process indexed by probability distributions. For this,
we provide a family of positive definite kernels built using transportation
based distances. We provide a probabilistic understanding of these kernels and
characterize the corresponding stochastic processes. We prove that the Gaussian
processes indexed by distributions corresponding to these kernels can be
efficiently forecast, opening new perspectives in Gaussian process modeling.
Multi-pseudo Regularized Label for Generated Data in Person Re-Identification
Authors: Yan Huang, Jinsong Xu, Qiang Wu, Zhedong Zheng, Zhao-Xiang Zhang, Jian Zhang
Date: 2018-01-21 00:00:00
Tasks: Generative Adversarial Network, Person Re-Identification, Pseudo Label

Sufficient training data normally is required to train deeply learned models.
However, due to the expensive manual process for labelling large number of
images, the amount of available training data is always limited. To produce
more data for training a deep network, Generative Adversarial Network (GAN) can
be used to generate artificial sample data. However, the generated data usually
does not have annotation labels. To solve this problem, in this paper, we
propose a virtual label called Multi-pseudo Regularized Label (MpRL) and assign
it to the generated data. With MpRL, the generated data will be used as the
supplementary of real training data to train a deep neural network in a
semi-supervised learning fashion. To build the corresponding relationship
between the real data and generated data, MpRL assigns each generated data a
proper virtual label which reflects the likelihood of the affiliation of the
generated data to pre-defined training classes in the real data domain. Unlike
the traditional label which usually is a single integral number, the virtual
label proposed in this work is a set of weight-based values each individual of
which is a number in (0,1] called multi-pseudo label and reflects the degree of
relation between each generated data to every pre-defined class of real data. A
comprehensive evaluation is carried out by adopting two state-of-the-art
convolutional neural networks (CNNs) in our experiments to verify the
effectiveness of MpRL. Experiments demonstrate that by assigning MpRL to
generated data, we can further improve the person re-ID performance on five
re-ID datasets, i.e., Market-1501, DukeMTMC-reID, CUHK03, VIPeR, and CUHK01.
The proposed method obtains +6.29%, +6.30%, +5.58%, +5.84%, and +3.48%
improvements in rank-1 accuracy over a strong CNN baseline on the five datasets
respectively, and outperforms state-of-the-art methods.
Supersaliency: A Novel Pipeline for Predicting Smooth Pursuit-Based Attention Improves Generalizability of Video Saliency
Authors: Mikhail Startsev, Michael Dorr
Date: 2018-01-26 00:00:00
Tasks: Prediction, Saliency Prediction

Predicting attention is a popular topic at the intersection of human and
computer vision. However, even though most of the available video saliency data
sets and models claim to target human observers' fixations, they fail to
differentiate them from smooth pursuit (SP), a major eye movement type that is
unique to perception of dynamic scenes. In this work, we highlight the
importance of SP and its prediction (which we call supersaliency, due to
greater selectivity compared to fixations), and aim to make its distinction
from fixations explicit for computational models. To this end, we (i) use
algorithmic and manual annotations of SP and fixations for two well-established
video saliency data sets, (ii) train Slicing Convolutional Neural Networks for
saliency prediction on either fixation- or SP-salient locations, and (iii)
evaluate our and 26 publicly available dynamic saliency models on three data
sets against traditional saliency and supersaliency ground truth. Overall, our
models outperform the state of the art in both the new supersaliency and the
traditional saliency problem settings, for which literature models are
optimized. Importantly, on two independent data sets, our supersaliency model
shows greater generalization ability and outperforms all other models, even for
fixation prediction.
Learning Combinations of Activation Functions
Authors: Franco Manessi, Alessandro Rozza
Date: 2018-01-29 00:00:00

In the last decade, an active area of research has been devoted to design
novel activation functions that are able to help deep neural networks to
converge, obtaining better performance. The training procedure of these
architectures usually involves optimization of the weights of their layers
only, while non-linearities are generally pre-specified and their (possible)
parameters are usually considered as hyper-parameters to be tuned manually. In
this paper, we introduce two approaches to automatically learn different
combinations of base activation functions (such as the identity function, ReLU,
and tanh) during the training phase. We present a thorough comparison of our
novel approaches with well-known architectures (such as LeNet-5, AlexNet, and
ResNet-56) on three standard datasets (Fashion-MNIST, CIFAR-10, and
ILSVRC-2012), showing substantial improvements in the overall performance, such
as an increase in the top-1 accuracy for AlexNet on ILSVRC-2012 of 3.01
percentage points.
Continuous Space Reordering Models for Phrase-based MT
Authors: Nadir Durrani, Fahim Dalvi
Date: 2018-01-25 00:00:00
Proceeding: IWSLT 2017 12
Tasks: Decoder, POS

Bilingual sequence models improve phrase-based translation and reordering by
overcoming phrasal independence assumption and handling long range reordering.
However, due to data sparsity, these models often fall back to very small
context sizes. This problem has been previously addressed by learning sequences
over generalized representations such as POS tags or word clusters. In this
paper, we explore an alternative based on neural network models. More
concretely we train neuralized versions of lexicalized reordering and the
operation sequence models using feed-forward neural network. Our results show
improvements of up to 0.6 and 0.5 BLEU points on top of the baseline
German->English and English->German systems. We also observed improvements
compared to the systems that used POS tags and word clusters to train these
models. Because we modify the bilingual corpus to integrate reordering
operations, this allows us to also train a sequence-to-sequence neural MT model
having explicit reordering triggers. Our motivation was to directly enable
reordering information in the encoder-decoder framework, which otherwise relies
solely on the attention model to handle long range reordering. We tried both
coarser and fine-grained reordering operations. However, these experiments did
not yield any improvements over the baseline Neural MT systems.
On Structured Prediction Theory with Calibrated Convex Surrogate Losses
Authors: Anton Osokin, Francis Bach, Simon Lacoste-Julien
Date: 2017-03-07 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Prediction, Structured Prediction

We provide novel theoretical insights on structured prediction in the context
of efficient convex surrogate loss minimization with consistency guarantees.
For any task loss, we construct a convex surrogate that can be optimized via
stochastic gradient descent and we prove tight bounds on the so-called
"calibration function" relating the excess surrogate risk to the actual risk.
In contrast to prior related work, we carefully monitor the effect of the
exponential number of classes in the learning guarantees as well as on the
optimization complexity. As an interesting consequence, we formalize the
intuition that some task losses make learning harder than others, and that the
classical 0-1 loss is ill-suited for general structured prediction.
Tournament Leave-pair-out Cross-validation for Receiver Operating Characteristic (ROC) Analysis
Authors: Ileana Montoya Perez, Antti Airola, Peter J. Boström, Ivan Jambor, Tapio Pahikkala
Date: 2018-01-29 00:00:00
Tasks: Diagnostic, Specificity

Receiver operating characteristic (ROC) analysis is widely used for evaluating diagnostic systems. Recent studies have shown that estimating an area under ROC curve (AUC) with standard cross-validation methods suffers from a large bias. The leave-pair-out (LPO) cross-validation has been shown to correct this bias. However, while LPO produces an almost unbiased estimate of AUC, it does not provide a ranking of the data needed for plotting and analyzing the ROC curve. In this study, we propose a new method called tournament leave-pair-out (TLPO) cross-validation. This method extends LPO by creating a tournament from pair comparisons to produce a ranking for the data. TLPO preserves the advantage of LPO for estimating AUC, while it also allows performing ROC analyses. We have shown using both synthetic and real world data that TLPO is as reliable as LPO for AUC estimation, and confirmed the bias in leave-one-out cross-validation on low-dimensional data. As a case study on ROC analysis, we also evaluate how reliably sensitivity and specificity can be estimated from TLPO ROC curves.
FReLU: Flexible Rectified Linear Units for Improving Convolutional Neural Networks
Authors: Suo Qiu, Xiangmin Xu, Bolun Cai
Date: 2017-06-25 00:00:00
Tasks: image-classification, Image Classification

Rectified linear unit (ReLU) is a widely used activation function for deep
convolutional neural networks. However, because of the zero-hard rectification,
ReLU networks miss the benefits from negative values. In this paper, we propose
a novel activation function called \emph{flexible rectified linear unit
(FReLU)} to further explore the effects of negative values. By redesigning the
rectified point of ReLU as a learnable parameter, FReLU expands the states of
the activation output. When the network is successfully trained, FReLU tends to
converge to a negative value, which improves the expressiveness and thus the
performance. Furthermore, FReLU is designed to be simple and effective without
exponential functions to maintain low cost computation. For being able to
easily used in various network architectures, FReLU does not rely on strict
assumptions by self-adaption. We evaluate FReLU on three standard image
classification datasets, including CIFAR-10, CIFAR-100, and ImageNet.
Experimental results show that the proposed method achieves fast convergence
and higher performances on both plain and residual networks.
Approximate Vanishing Ideal via Data Knotting
Authors: Hiroshi Kera, Yoshihiko Hasegawa
Date: 2018-01-29 00:00:00
Tasks: Classification, General Classification

The vanishing ideal is a set of polynomials that takes zero value on the
given data points. Originally proposed in computer algebra, the vanishing ideal
has been recently exploited for extracting the nonlinear structures of data in
many applications. To avoid overfitting to noisy data, the polynomials are
often designed to approximately rather than exactly equal zero on the
designated data. Although such approximations empirically demonstrate high
performance, the sound algebraic structure of the vanishing ideal is lost. The
present paper proposes a vanishing ideal that is tolerant to noisy data and
also pursued to have a better algebraic structure. As a new problem, we
simultaneously find a set of polynomials and data points for which the
polynomials approximately vanish on the input data points, and almost exactly
vanish on the discovered data points. In experimental classification tests, our
method discovered much fewer and lower-degree polynomials than an existing
state-of-the-art method. Consequently, our method accelerated the runtime of
the classification tasks without degrading the classification accuracy.
Comparative Study of ECO and CFNet Trackers in Noisy Environment
Authors: Mustansar Fiaz, Sajid Javed, Arif Mahmood, Soon Ki Jung
Date: 2018-01-29 00:00:00
Tasks: Object Tracking, Visual Object Tracking

Object tracking is one of the most challenging task and has secured
significant attention of computer vision researchers in the past two decades.
Recent deep learning based trackers have shown good performance on various
tracking challenges. A tracking method should track objects in sequential
frames accurately in challenges such as deformation, low resolution, occlusion,
scale and light variations. Most trackers achieve good performance on specific
challenges instead of all tracking problems, hence there is a lack of general
purpose tracking algorithms that can perform well in all conditions. Moreover,
performance of tracking techniques has not been evaluated in noisy
environments. Visual object tracking has real world applications and there is
good chance that noise may get added during image acquisition in surveillance
cameras. We aim to study the robustness of two state of the art trackers in the
presence of noise including Efficient Convolutional Operators (ECO) and
Correlation Filter Network (CFNet). Our study demonstrates that the performance
of these trackers degrades as the noise level increases, which demonstrate the
need to design more robust tracking algorithms.
Game of Sketches: Deep Recurrent Models of Pictionary-style Word Guessing
Authors: Ravi Kiran Sarvadevabhatla, Shiv Surya, Trisha Mittal, Venkatesh Babu Radhakrishnan
Date: 2018-01-29 00:00:00
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

The ability of intelligent agents to play games in human-like fashion is
popularly considered a benchmark of progress in Artificial Intelligence.
Similarly, performance on multi-disciplinary tasks such as Visual Question
Answering (VQA) is considered a marker for gauging progress in Computer Vision.
In our work, we bring games and VQA together. Specifically, we introduce the
first computational model aimed at Pictionary, the popular word-guessing social
game. We first introduce Sketch-QA, an elementary version of Visual Question
Answering task. Styled after Pictionary, Sketch-QA uses incrementally
accumulated sketch stroke sequences as visual data. Notably, Sketch-QA involves
asking a fixed question ("What object is being drawn?") and gathering
open-ended guess-words from human guessers. We analyze the resulting dataset
and present many interesting findings therein. To mimic Pictionary-style
guessing, we subsequently propose a deep neural model which generates
guess-words in response to temporally evolving human-drawn sketches. Our model
even makes human-like mistakes while guessing, thus amplifying the human
mimicry factor. We evaluate our model on the large-scale guess-word dataset
generated via Sketch-QA task and compare with various baselines. We also
conduct a Visual Turing Test to obtain human impressions of the guess-words
generated by humans and our model. Experimental results demonstrate the promise
of our approach for Pictionary and similarly themed games.
Speeding Up Distributed Machine Learning Using Codes
Authors: Kangwook Lee, Maximilian Lam, Ramtin Pedarsani, Dimitris Papailiopoulos, Kannan Ramchandran
Date: 2015-12-08 00:00:00
Tasks: BIG-bench Machine Learning

Codes are widely used in many engineering applications to offer robustness
against noise. In large-scale systems there are several types of noise that can
affect the performance of distributed machine learning algorithms -- straggler
nodes, system failures, or communication bottlenecks -- but there has been
little interaction cutting across codes, machine learning, and distributed
systems. In this work, we provide theoretical insights on how coded solutions
can achieve significant gains compared to uncoded ones. We focus on two of the
most basic building blocks of distributed learning algorithms: matrix
multiplication and data shuffling. For matrix multiplication, we use codes to
alleviate the effect of stragglers, and show that if the number of homogeneous
workers is $n$, and the runtime of each subtask has an exponential tail, coded
computation can speed up distributed matrix multiplication by a factor of $\log
n$. For data shuffling, we use codes to reduce communication bottlenecks,
exploiting the excess in storage. We show that when a constant fraction
$\alpha$ of the data matrix can be cached at each worker, and $n$ is the number
of workers, \emph{coded shuffling} reduces the communication cost by a factor
of $(\alpha + \frac{1}{n})\gamma(n)$ compared to uncoded shuffling, where
$\gamma(n)$ is the ratio of the cost of unicasting $n$ messages to $n$ users to
multicasting a common message (of the same size) to $n$ users. For instance,
$\gamma(n) \simeq n$ if multicasting a message to $n$ users is as cheap as
unicasting a message to one user. We also provide experiment results,
corroborating our theoretical gains of the coded algorithms.