pwc-archive/papers-with-abstracts huggingface dataset:
Structured Dropout for Weak Label and Multi-Instance Learning and Its Application to Score-Informed Source Separation
Authors: Sebastian Ewert, Mark B. Sandler
Date: 2016-09-15 00:00:00
Tasks: Representation Learning

Many success stories involving deep neural networks are instances of
supervised learning, where available labels power gradient-based learning
methods. Creating such labels, however, can be expensive and thus there is
increasing interest in weak labels which only provide coarse information, with
uncertainty regarding time, location or value. Using such labels often leads to
considerable challenges for the learning process. Current methods for
weak-label training often employ standard supervised approaches that
additionally reassign or prune labels during the learning process. The
information gain, however, is often limited as only the importance of labels
where the network already yields reasonable results is boosted. We propose
treating weak-label training as an unsupervised problem and use the labels to
guide the representation learning to induce structure. To this end, we propose
two autoencoder extensions: class activity penalties and structured dropout. We
demonstrate the capabilities of our approach in the context of score-informed
source separation of music.
Clustering Algorithms: A Comparative Approach
Authors: Mayra Z. Rodriguez, Cesar H. Comin, Dalcimar Casanova, Odemir M. Bruno, Diego R. Amancio, Francisco A. Rodrigues, Luciano da F. Costa
Date: 2016-12-26 00:00:00
Tasks: Clustering

Many real-world systems can be studied in terms of pattern recognition tasks,
so that proper use (and understanding) of machine learning methods in practical
applications becomes essential. While a myriad of classification methods have
been proposed, there is no consensus on which methods are more suitable for a
given dataset. As a consequence, it is important to comprehensively compare
methods in many possible scenarios. In this context, we performed a systematic
comparison of 7 well-known clustering methods available in the R language. In
order to account for the many possible variations of data, we considered
artificial datasets with several tunable properties (number of classes,
separation between classes, etc). In addition, we also evaluated the
sensitivity of the clustering methods with regard to their parameters
configuration. The results revealed that, when considering the default
configurations of the adopted methods, the spectral approach usually
outperformed the other clustering algorithms. We also found that the default
configuration of the adopted implementations was not accurate. In these cases,
a simple approach based on random selection of parameters values proved to be a
good alternative to improve the performance. All in all, the reported approach
provides subsidies guiding the choice of clustering algorithms.
Abstractive Headline Generation for Spoken Content by Attentive Recurrent Neural Networks with ASR Error Modeling
Authors: Lang-Chi Yu, Hung-Yi Lee, Lin-shan Lee
Date: 2016-12-26 00:00:00
Tasks: Abstractive Text Summarization, Document Summarization, Headline Generation

Headline generation for spoken content is important since spoken content is
difficult to be shown on the screen and browsed by the user. It is a special
type of abstractive summarization, for which the summaries are generated word
by word from scratch without using any part of the original content. Many deep
learning approaches for headline generation from text document have been
proposed recently, all requiring huge quantities of training data, which is
difficult for spoken document summarization. In this paper, we propose an ASR
error modeling approach to learn the underlying structure of ASR error patterns
and incorporate this model in an Attentive Recurrent Neural Network (ARNN)
architecture. In this way, the model for abstractive headline generation for
spoken content can be learned from abundant text data and the ASR data for some
recognizers. Experiments showed very encouraging results and verified that the
proposed ASR error model works well even when the input spoken content is
recognized by a recognizer very different from the one the model learned from.
Deep convolutional neural networks for predominant instrument recognition in polyphonic music
Authors: Yoonchang Han, Jaehun Kim, Kyogu Lee
Date: 2016-05-31 00:00:00
Tasks: Information Retrieval, Instrument Recognition, Music Information Retrieval, Music Transcription, Retrieval

Identifying musical instruments in polyphonic music recordings is a
challenging but important problem in the field of music information retrieval.
It enables music search by instrument, helps recognize musical genres, or can
make music transcription easier and more accurate. In this paper, we present a
convolutional neural network framework for predominant instrument recognition
in real-world polyphonic music. We train our network from fixed-length music
excerpts with a single-labeled predominant instrument and estimate an arbitrary
number of predominant instruments from an audio signal with a variable length.
To obtain the audio-excerpt-wise result, we aggregate multiple outputs from
sliding windows over the test audio. In doing so, we investigated two different
aggregation methods: one takes the average for each instrument and the other
takes the instrument-wise sum followed by normalization. In addition, we
conducted extensive experiments on several important factors that affect the
performance, including analysis window size, identification threshold, and
activation functions for neural networks to find the optimal set of parameters.
Using a dataset of 10k audio excerpts from 11 instruments for evaluation, we
found that convolutional neural networks are more robust than conventional
methods that exploit spectral features and source separation with support
vector machines. Experimental results showed that the proposed convolutional
network architecture obtained an F1 measure of 0.602 for micro and 0.503 for
macro, respectively, achieving 19.6% and 16.4% in performance improvement
compared with other state-of-the-art algorithms.
FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics
Authors: Tran Minh Quan, David G. C. Hildebrand, Won-Ki Jeong
Date: 2016-12-16 00:00:00
Tasks: Brain Image Segmentation, image-classification, Image Classification, Image Segmentation, Segmentation, Semantic Segmentation

Electron microscopic connectomics is an ambitious research direction with the
goal of studying comprehensive brain connectivity maps by using
high-throughput, nano-scale microscopy. One of the main challenges in
connectomics research is developing scalable image analysis algorithms that
require minimal user intervention. Recently, deep learning has drawn much
attention in computer vision because of its exceptional performance in image
classification tasks. For this reason, its application to connectomic analyses
holds great promise, as well. In this paper, we introduce a novel deep neural
network architecture, FusionNet, for the automatic segmentation of neuronal
structures in connectomics data. FusionNet leverages the latest advances in
machine learning, such as semantic segmentation and residual neural networks,
with the novel introduction of summation-based skip connections to allow a much
deeper network architecture for a more accurate segmentation. We demonstrate
the performance of the proposed method by comparing it with state-of-the-art
electron microscopy (EM) segmentation methods from the ISBI EM segmentation
challenge. We also show the segmentation results on two different tasks
including cell membrane and cell body segmentation and a statistical analysis
of cell morphology.
Multivariate Industrial Time Series with Cyber-Attack Simulation: Fault Detection Using an LSTM-based Predictive Data Model
Authors: Pavel Filonov, Andrey Lavrentyev, Artem Vorontsov
Date: 2016-12-20 00:00:00
Tasks: Fault Detection, Time Series, Time Series Analysis

We adopted an approach based on an LSTM neural network to monitor and detect
faults in industrial multivariate time series data. To validate the approach we
created a Modelica model of part of a real gasoil plant. By introducing hacks
into the logic of the Modelica model, we were able to generate both the roots
and causes of fault behavior in the plant. Having a self-consistent data set
with labeled faults, we used an LSTM architecture with a forecasting error
threshold to obtain precision and recall quality metrics. The dependency of the
quality metric on the threshold level is considered. An appropriate mechanism
such as "one handle" was introduced for filtering faults that are outside of
the plant operator field of interest.
Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters
Authors: AJ Piergiovanni, Chenyou Fan, Michael S. Ryoo
Date: 2016-05-26 00:00:00
Tasks: Action Classification, Action Recognition In Videos, Activity Recognition, Activity Recognition In Videos, Human Activity Recognition

In this paper, we newly introduce the concept of temporal attention filters,
and describe how they can be used for human activity recognition from videos.
Many high-level activities are often composed of multiple temporal parts (e.g.,
sub-events) with different duration/speed, and our objective is to make the
model explicitly learn such temporal structure using multiple attention filters
and benefit from them. Our temporal filters are designed to be fully
differentiable, allowing end-of-end training of the temporal filters together
with the underlying frame-based or segment-based convolutional neural network
architectures. This paper presents an approach of learning a set of optimal
static temporal attention filters to be shared across different videos, and
extends this approach to dynamically adjust attention filters per testing video
using recurrent long short-term memory networks (LSTMs). This allows our
temporal attention filters to learn latent sub-events specific to each
activity. We experimentally confirm that the proposed concept of temporal
attention filters benefits the activity recognition, and we visualize the
learned latent sub-events.
Privacy-Preserving Human Activity Recognition from Extreme Low Resolution
Authors: Michael S. Ryoo, Brandon Rothrock, Charles Fleming, Hyun Jong Yang
Date: 2016-04-12 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Privacy Preserving, Super-Resolution

Privacy protection from surreptitious video recordings is an important
societal challenge. We desire a computer vision system (e.g., a robot) that can
recognize human activities and assist our daily life, yet ensure that it is not
recording video that may invade our privacy. This paper presents a fundamental
approach to address such contradicting objectives: human activity recognition
while only using extreme low-resolution (e.g., 16x12) anonymized videos. We
introduce the paradigm of inverse super resolution (ISR), the concept of
learning the optimal set of image transformations to generate multiple
low-resolution (LR) training videos from a single video. Our ISR learns
different types of sub-pixel transformations optimized for the activity
classification, allowing the classifier to best take advantage of existing
high-resolution videos (e.g., YouTube videos) by creating multiple LR training
videos tailored for the problem. We experimentally confirm that the paradigm of
inverse super resolution is able to benefit activity recognition from extreme
low-resolution videos.
Image-Text Multi-Modal Representation Learning by Adversarial Backpropagation
Authors: Gwangbeen Park, Woobin Im
Date: 2016-12-26 00:00:00
Tasks: Representation Learning

We present novel method for image-text multi-modal representation learning.
In our knowledge, this work is the first approach of applying adversarial
learning concept to multi-modal learning and not exploiting image-text pair
information to learn multi-modal feature. We only use category information in
contrast with most previous methods using image-text pair information for
multi-modal embedding. In this paper, we show that multi-modal feature can be
achieved without image-text pair information and our method makes more similar
distribution with image and text in multi-modal feature space than other
methods which use image-text pair information. And we show our multi-modal
feature has universal semantic information, even though it was trained for
category prediction. Our model is end-to-end backpropagation, intuitive and
easily extended to other multi-modal learning work.
Generalization of ERM in Stochastic Convex Optimization: The Dimension Strikes Back
Authors: Vitaly Feldman
Date: 2016-08-15 00:00:00
Proceeding: NeurIPS 2016 12

In stochastic convex optimization the goal is to minimize a convex function
$F(x) \doteq {\mathbf E}_{{\mathbf f}\sim D}[{\mathbf f}(x)]$ over a convex set
$\cal K \subset {\mathbb R}^d$ where $D$ is some unknown distribution and each
$f(\cdot)$ in the support of $D$ is convex over $\cal K$. The optimization is
commonly based on i.i.d.~samples $f^1,f^2,\ldots,f^n$ from $D$. A standard
approach to such problems is empirical risk minimization (ERM) that optimizes
$F_S(x) \doteq \frac{1}{n}\sum_{i\leq n} f^i(x)$. Here we consider the question
of how many samples are necessary for ERM to succeed and the closely related
question of uniform convergence of $F_S$ to $F$ over $\cal K$. We demonstrate
that in the standard $\ell_p/\ell_q$ setting of Lipschitz-bounded functions
over a $\cal K$ of bounded radius, ERM requires sample size that scales
linearly with the dimension $d$. This nearly matches standard upper bounds and
improves on $\Omega(\log d)$ dependence proved for $\ell_2/\ell_2$ setting by
Shalev-Shwartz et al. (2009). In stark contrast, these problems can be solved
using dimension-independent number of samples for $\ell_2/\ell_2$ setting and
$\log d$ dependence for $\ell_1/\ell_\infty$ setting using other approaches. We
further show that our lower bound applies even if the functions in the support
of $D$ are smooth and efficiently computable and even if an $\ell_1$
regularization term is added. Finally, we demonstrate that for a more general
class of bounded-range (but not Lipschitz-bounded) stochastic convex programs
an infinite gap appears already in dimension 2.
Deep Multimodal Feature Analysis for Action Recognition in RGB+D Videos
Authors: Amir Shahroudy, Tian-Tsong Ng, Yihong Gong, Gang Wang
Date: 2016-03-23 00:00:00
Tasks: Action Classification, Action Recognition, General Classification, Multimodal Activity Recognition, Temporal Action Localization

Single modality action recognition on RGB or depth sequences has been
extensively explored recently. It is generally accepted that each of these two
modalities has different strengths and limitations for the task of action
recognition. Therefore, analysis of the RGB+D videos can help us to better
study the complementary properties of these two types of modalities and achieve
higher levels of performance. In this paper, we propose a new deep autoencoder
based shared-specific feature factorization network to separate input
multimodal signals into a hierarchy of components. Further, based on the
structure of the features, a structured sparsity learning machine is proposed
which utilizes mixed norms to apply regularization within components and group
selection between them for better classification performance. Our experimental
results show the effectiveness of our cross-modality feature analysis framework
by achieving state-of-the-art accuracy for action classification on five
challenging benchmark datasets.
Echo State Condition at the Critical Point
Authors: Norbert Michael Mayer
Date: 2014-11-25 00:00:00

Recurrent networks with transfer functions that fulfill the Lipschitz
continuity with K=1 may be echo state networks if certain limitations on the
recurrent connectivity are applied. It has been shown that it is sufficient if
the largest singular value of the recurrent connectivity is smaller than 1. The
main achievement of this paper is a proof under which conditions the network is
an echo state network even if the largest singular value is one. It turns out
that in this critical case the exact shape of the transfer function plays a
decisive role in determining whether the network still fulfills the echo state
condition. In addition, several examples with one neuron networks are outlined
to illustrate effects of critical connectivity. Moreover, within the manuscript
a mathematical definition for a critical echo state network is suggested.
Multi-Task Curriculum Transfer Deep Learning of Clothing Attributes
Authors: Qi Dong, Shaogang Gong, Xiatian Zhu
Date: 2016-10-12 00:00:00
Tasks: Attribute, Deep Learning, Transfer Learning

Recognising detailed clothing characteristics (fine-grained attributes) in
unconstrained images of people in-the-wild is a challenging task for computer
vision, especially when there is only limited training data from the wild
whilst most data available for model learning are captured in well-controlled
environments using fashion models (well lit, no background clutter, frontal
view, high-resolution). In this work, we develop a deep learning framework
capable of model transfer learning from well-controlled shop clothing images
collected from web retailers to in-the-wild images from the street.
Specifically, we formulate a novel Multi-Task Curriculum Transfer (MTCT) deep
learning method to explore multiple sources of different types of web
annotations with multi-labelled fine-grained attributes. Our multi-task loss
function is designed to extract more discriminative representations in training
by jointly learning all attributes, and our curriculum strategy exploits the
staged easy-to-complex transfer learning motivated by cognitive studies. We
demonstrate the advantages of the MTCT model over the state-of-the-art methods
on the X-Domain benchmark, a large scale clothing attribute dataset. Moreover,
we show that the MTCT model has a notable advantage over contemporary models
when the training data size is small.
Congruences and Concurrent Lines in Multi-View Geometry
Authors: Jean Ponce, Bernd Sturmfels, Matthew Trager
Date: 2016-08-21 00:00:00

We present a new framework for multi-view geometry in computer vision. A
camera is a mapping between $\mathbb{P}^3$ and a line congruence. This model,
which ignores image planes and measurements, is a natural abstraction of
traditional pinhole cameras. It includes two-slit cameras, pushbroom cameras,
catadioptric cameras, and many more. We study the concurrent lines variety,
which consists of $n$-tuples of lines in $\mathbb{P}^3$ that intersect at a
point. Combining its equations with those of various congruences, we derive
constraints for corresponding images in multiple views. We also study
photographic cameras which use image measurements and are modeled as rational
maps from $\mathbb{P}^3$ to $\mathbb{P}^2$ or $\mathbb{P}^1\times
\mathbb{P}^1$.
Globally Optimal Object Tracking with Fully Convolutional Networks
Authors: Jinho Lee, Brian Kenji Iwana, Shouta Ide, Seiichi Uchida
Date: 2016-12-25 00:00:00
Tasks: Object, Object Tracking

Tracking is one of the most important but still difficult tasks in computer
vision and pattern recognition. The main difficulties in the tracking field are
appearance variation and occlusion. Most traditional tracking methods set the
parameters or templates to track target objects in advance and should be
modified accordingly. Thus, we propose a new and robust tracking method using a
Fully Convolutional Network (FCN) to obtain an object probability map and
Dynamic Programming (DP) to seek the globally optimal path through all frames
of video. Our proposed method solves the object appearance variation problem
with the use of a FCN and deals with occlusion by DP. We show that our method
is effective in tracking various single objects through video frames.
Reinforcement Learning Using Quantum Boltzmann Machines
Authors: Daniel Crawford, Anna Levit, Navid Ghadermarzy, Jaspreet S. Oberoi, Pooya Ronagh
Date: 2016-12-17 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We investigate whether quantum annealers with select chip layouts can
outperform classical computers in reinforcement learning tasks. We associate a
transverse field Ising spin Hamiltonian with a layout of qubits similar to that
of a deep Boltzmann machine (DBM) and use simulated quantum annealing (SQA) to
numerically simulate quantum sampling from this system. We design a
reinforcement learning algorithm in which the set of visible nodes representing
the states and actions of an optimal policy are the first and last layers of
the deep network. In absence of a transverse field, our simulations show that
DBMs are trained more effectively than restricted Boltzmann machines (RBM) with
the same number of nodes. We then develop a framework for training the network
as a quantum Boltzmann machine (QBM) in the presence of a significant
transverse field for reinforcement learning. This method also outperforms the
reinforcement learning method that uses RBMs.
YOLO9000: Better, Faster, Stronger
Authors: Joseph Redmon, Ali Farhadi
Date: 2016-12-25 00:00:00
Proceeding: CVPR 2017 7
Tasks: 3D Object Detection, General Classification, Object, Object Counting, Object Detection, Real-Time Object Detection

We introduce YOLO9000, a state-of-the-art, real-time object detection system
that can detect over 9000 object categories. First we propose various
improvements to the YOLO detection method, both novel and drawn from prior
work. The improved model, YOLOv2, is state-of-the-art on standard detection
tasks like PASCAL VOC and COCO. At 67 FPS, YOLOv2 gets 76.8 mAP on VOC 2007. At
40 FPS, YOLOv2 gets 78.6 mAP, outperforming state-of-the-art methods like
Faster RCNN with ResNet and SSD while still running significantly faster.
Finally we propose a method to jointly train on object detection and
classification. Using this method we train YOLO9000 simultaneously on the COCO
detection dataset and the ImageNet classification dataset. Our joint training
allows YOLO9000 to predict detections for object classes that don't have
labelled detection data. We validate our approach on the ImageNet detection
task. YOLO9000 gets 19.7 mAP on the ImageNet detection validation set despite
only having detection data for 44 of the 200 classes. On the 156 classes not in
COCO, YOLO9000 gets 16.0 mAP. But YOLO can detect more than just 200 classes;
it predicts detections for more than 9000 different object categories. And it
still runs in real-time.