pwc-archive/papers-with-abstracts huggingface dataset:
SNIPER: Efficient Multi-Scale Training
Authors: Bharat Singh, Mahyar Najibi, Larry S. Davis
Date: 2018-05-23 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: GPU, image-classification, object-detection, Object Detection, Region Proposal

We present SNIPER, an algorithm for performing efficient multi-scale training
in instance level visual recognition tasks. Instead of processing every pixel
in an image pyramid, SNIPER processes context regions around ground-truth
instances (referred to as chips) at the appropriate scale. For background
sampling, these context-regions are generated using proposals extracted from a
region proposal network trained with a short learning schedule. Hence, the
number of chips generated per image during training adaptively changes based on
the scene complexity. SNIPER only processes 30% more pixels compared to the
commonly used single scale training at 800x1333 pixels on the COCO dataset.
But, it also observes samples from extreme resolutions of the image pyramid,
like 1400x2000 pixels. As SNIPER operates on resampled low resolution chips
(512x512 pixels), it can have a batch size as large as 20 on a single GPU even
with a ResNet-101 backbone. Therefore it can benefit from batch-normalization
during training without the need for synchronizing batch-normalization
statistics across GPUs. SNIPER brings training of instance level recognition
tasks like object detection closer to the protocol for image classification and
suggests that the commonly accepted guideline that it is important to train on
high resolution images for instance level visual recognition tasks might not be
correct. Our implementation based on Faster-RCNN with a ResNet-101 backbone
obtains an mAP of 47.6% on the COCO dataset for bounding box detection and can
process 5 images per second during inference with a single GPU. Code is
available at https://github.com/MahyarNajibi/SNIPER/.
Constraining the Dynamics of Deep Probabilistic Models
Authors: Marco Lorenzi, Maurizio Filippone
Date: 2018-02-15 00:00:00
Proceeding: ICML 2018 7
Tasks: Uncertainty Quantification, Variational Inference

We introduce a novel generative formulation of deep probabilistic models
implementing "soft" constraints on their function dynamics. In particular, we
develop a flexible methodological framework where the modeled functions and
derivatives of a given order are subject to inequality or equality constraints.
We then characterize the posterior distribution over model and constraint
parameters through stochastic variational inference. As a result, the proposed
approach allows for accurate and scalable uncertainty quantification on the
predictions and on all parameters. We demonstrate the application of equality
constraints in the challenging problem of parameter inference in ordinary
differential equation models, while we showcase the application of inequality
constraints on the problem of monotonic regression of count data. The proposed
approach is extensively tested in several experimental settings, leading to
highly competitive results in challenging modeling applications, while offering
high expressiveness, flexibility and scalability.
A Simple Reservoir Model of Working Memory with Real Values
Authors: Anthony Strock, Nicolas Rougier, Xavier Hinaut
Date: 2018-06-18 00:00:00

The prefrontal cortex is known to be involved in many high-level cognitive
functions, in particular, working memory. Here, we study to what extent a group
of randomly connected units (namely an Echo State Network, ESN) can store and
maintain (as output) an arbitrary real value from a streamed input, i.e. can
act as a sustained working memory unit. Furthermore, we explore to what extent
such an architecture can take advantage of the stored value in order to produce
non-linear computations. Comparison between different architectures (with and
without feedback, with and without a working memory unit) shows that an
explicit memory improves the performances.
Segmentation of Photovoltaic Module Cells in Uncalibrated Electroluminescence Images
Authors: Sergiu Deitsch, Claudia Buerhop-Lutz, Evgenii Sovetkin, Ansgar Steland, Andreas Maier, Florian Gallwitz, Christian Riess
Date: 2018-06-18 00:00:00
Tasks: Segmentation, Solar Cell Segmentation

High resolution electroluminescence (EL) images captured in the infrared spectrum allow to visually and non-destructively inspect the quality of photovoltaic (PV) modules. Currently, however, such a visual inspection requires trained experts to discern different kinds of defects, which is time-consuming and expensive. Automated segmentation of cells is therefore a key step in automating the visual inspection workflow. In this work, we propose a robust automated segmentation method for extraction of individual solar cells from EL images of PV modules. This enables controlled studies on large amounts of data to understanding the effects of module degradation over time-a process not yet fully understood. The proposed method infers in several steps a high-level solar module representation from low-level edge features. An important step in the algorithm is to formulate the segmentation problem in terms of lens calibration by exploiting the plumbline constraint. We evaluate our method on a dataset of various solar modules types containing a total of 408 solar cells with various defects. Our method robustly solves this task with a median weighted Jaccard index of 94.47% and an $F_1$ score of 97.62%, both indicating a very high similarity between automatically segmented and ground truth solar cell masks.
Dual Recovery Network with Online Compensation for Image Super-Resolution
Authors: Sifeng Xia, Wenhan Yang, Jiaying Liu, Zongming Guo
Date: 2017-01-20 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Image super-resolution (SR) methods essentially lead to a loss of some
high-frequency (HF) information when predicting high-resolution (HR) images
from low-resolution (LR) images without using external references. To address
this issue, we additionally utilize online retrieved data to facilitate image
SR in a unified deep framework. A novel dual high-frequency recovery network
(DHN) is proposed to predict an HR image with three parts: an LR image, an
internal inferred HF (IHF) map (HF missing part inferred solely from the LR
image) and an external extracted HF (EHF) map. In particular, we infer the HF
information based on both the LR image and similar HR references which are
retrieved online. For the EHF map, we align the references with affine
transformation and then in the aligned references, part of HF signals are
extracted by the proposed DHN to compensate for the HF loss. Extensive
experimental results demonstrate that our DHN achieves notably better
performance than state-of-the-art SR methods.
HitNet: a neural network with capsules embedded in a Hit-or-Miss layer, extended with hybrid data augmentation and ghost capsules
Authors: Adrien Deliège, Anthony Cioppa, Marc Van Droogenbroeck
Date: 2018-06-18 00:00:00
Tasks: Data Augmentation

Neural networks designed for the task of classification have become a
commodity in recent years. Many works target the development of better
networks, which results in a complexification of their architectures with more
layers, multiple sub-networks, or even the combination of multiple classifiers.
In this paper, we show how to redesign a simple network to reach excellent
performances, which are better than the results reproduced with CapsNet on
several datasets, by replacing a layer with a Hit-or-Miss layer. This layer
contains activated vectors, called capsules, that we train to hit or miss a
central capsule by tailoring a specific centripetal loss function. We also show
how our network, named HitNet, is capable of synthesizing a representative
sample of the images of a given class by including a reconstruction network.
This possibility allows to develop a data augmentation step combining
information from the data space and the feature space, resulting in a hybrid
data augmentation process. In addition, we introduce the possibility for
HitNet, to adopt an alternative to the true target when needed by using the new
concept of ghost capsules, which is used here to detect potentially mislabeled
images in the training data.
The Information Autoencoding Family: A Lagrangian Perspective on Latent Variable Generative Models
Authors: Shengjia Zhao, Jiaming Song, Stefano Ermon
Date: 2018-06-18 00:00:00

A large number of objectives have been proposed to train latent variable
generative models. We show that many of them are Lagrangian dual functions of
the same primal optimization problem. The primal problem optimizes the mutual
information between latent and visible variables, subject to the constraints of
accurately modeling the data distribution and performing correct amortized
inference. By choosing to maximize or minimize mutual information, and choosing
different Lagrange multipliers, we obtain different objectives including
InfoGAN, ALI/BiGAN, ALICE, CycleGAN, beta-VAE, adversarial autoencoders, AVB,
AS-VAE and InfoVAE. Based on this observation, we provide an exhaustive
characterization of the statistical and computational trade-offs made by all
the training objectives in this class of Lagrangian duals. Next, we propose a
dual optimization method where we optimize model parameters as well as the
Lagrange multipliers. This method achieves Pareto optimal solutions in terms of
optimizing information and satisfying the constraints.
Semi-tied Units for Efficient Gating in LSTM and Highway Networks
Authors: Chao Zhang, Philip Woodland
Date: 2018-06-18 00:00:00
Tasks: speech-recognition, Speech Recognition

Gating is a key technique used for integrating information from multiple
sources by long short-term memory (LSTM) models and has recently also been
applied to other models such as the highway network. Although gating is
powerful, it is rather expensive in terms of both computation and storage as
each gating unit uses a separate full weight matrix. This issue can be severe
since several gates can be used together in e.g. an LSTM cell. This paper
proposes a semi-tied unit (STU) approach to solve this efficiency issue, which
uses one shared weight matrix to replace those in all the units in the same
layer. The approach is termed "semi-tied" since extra parameters are used to
separately scale each of the shared output values. These extra scaling factors
are associated with the network activation functions and result in the use of
parameterised sigmoid, hyperbolic tangent, and rectified linear unit functions.
Speech recognition experiments using British English multi-genre broadcast data
showed that using STUs can reduce the calculation and storage cost by a factor
of three for highway networks and four for LSTMs, while giving similar word
error rates to the original models.
Predicting Citation Counts with a Neural Network
Authors: Tobias Mistele, Tom Price, Sabine Hossenfelder
Date: 2018-06-12 00:00:00

We here describe and present results of a simple neural network that predicts
individual researchers' future citation counts based on a variety of data from
the researchers' past. For publications available on the open access-server
arXiv.org we find a higher predictability than previous studies.
An Ensemble of Transfer, Semi-supervised and Supervised Learning Methods for Pathological Heart Sound Classification
Authors: Ahmed Imtiaz Humayun, Md. Tauhiduzzaman Khan, Shabnam Ghaffarzadegan, Zhe Feng, Taufiq Hasan
Date: 2018-06-18 00:00:00
Tasks: General Classification, Representation Learning, Sound Classification

In this work, we propose an ensemble of classifiers to distinguish between
various degrees of abnormalities of the heart using Phonocardiogram (PCG)
signals acquired using digital stethoscopes in a clinical setting, for the
INTERSPEECH 2018 Computational Paralinguistics (ComParE) Heart Beats
SubChallenge. Our primary classification framework constitutes a convolutional
neural network with 1D-CNN time-convolution (tConv) layers, which uses features
transferred from a model trained on the 2016 Physionet Heart Sound Database. We
also employ a Representation Learning (RL) approach to generate features in an
unsupervised manner using Deep Recurrent Autoencoders and use Support Vector
Machine (SVM) and Linear Discriminant Analysis (LDA) classifiers. Finally, we
utilize an SVM classifier on a high-dimensional segment-level feature extracted
using various functionals on short-term acoustic features, i.e., Low-Level
Descriptors (LLD). An ensemble of the three different approaches provides a
relative improvement of 11.13% compared to our best single sub-system in terms
of the Unweighted Average Recall (UAR) performance metric on the evaluation
dataset.
A unified strategy for implementing curiosity and empowerment driven reinforcement learning
Authors: Ildefons Magrans de Abril, Ryota Kanai
Date: 2018-06-18 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Although there are many approaches to implement intrinsically motivated
artificial agents, the combined usage of multiple intrinsic drives remains
still a relatively unexplored research area. Specifically, we hypothesize that
a mechanism capable of quantifying and controlling the evolution of the
information flow between the agent and the environment could be the fundamental
component for implementing a higher degree of autonomy into artificial
intelligent agents. This paper propose a unified strategy for implementing two
semantically orthogonal intrinsic motivations: curiosity and empowerment.
Curiosity reward informs the agent about the relevance of a recent agent
action, whereas empowerment is implemented as the opposite information flow
from the agent to the environment that quantifies the agent's potential of
controlling its own future. We show that an additional homeostatic drive is
derived from the curiosity reward, which generalizes and enhances the
information gain of a classical curious/heterostatic reinforcement learning
agent. We show how a shared internal model by curiosity and empowerment
facilitates a more efficient training of the empowerment function. Finally, we
discuss future directions for further leveraging the interplay between these
two intrinsic rewards.
Multi-Modal Data Augmentation for End-to-End ASR
Authors: Adithya Renduchintala, Shuoyang Ding, Matthew Wiesner, Shinji Watanabe
Date: 2018-03-27 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Data Augmentation, Decoder, Language Modeling, Language Modelling, speech-recognition, Speech Recognition

We present a new end-to-end architecture for automatic speech recognition
(ASR) that can be trained using \emph{symbolic} input in addition to the
traditional acoustic input. This architecture utilizes two separate encoders:
one for acoustic input and another for symbolic input, both sharing the
attention and decoder parameters. We call this architecture a multi-modal data
augmentation network (MMDA), as it can support multi-modal (acoustic and
symbolic) input and enables seamless mixing of large text datasets with
significantly smaller transcribed speech corpora during training. We study
different ways of transforming large text corpora into a symbolic form suitable
for training our MMDA network. Our best MMDA setup obtains small improvements
on character error rate (CER), and as much as 7-10\% relative word error rate
(WER) improvement over a baseline both with and without an external language
model.
Deforming Autoencoders: Unsupervised Disentangling of Shape and Appearance
Authors: Zhixin Shu, Mihir Sahasrabudhe, Alp Guler, Dimitris Samaras, Nikos Paragios, Iasonas Kokkinos
Date: 2018-06-18 00:00:00
Proceeding: ECCV 2018 9
Tasks: Unsupervised Facial Landmark Detection

In this work we introduce Deforming Autoencoders, a generative model for
images that disentangles shape from appearance in an unsupervised manner. As in
the deformable template paradigm, shape is represented as a deformation between
a canonical coordinate system (`template') and an observed image, while
appearance is modeled in `canonical', template, coordinates, thus discarding
variability due to deformations. We introduce novel techniques that allow this
approach to be deployed in the setting of autoencoders and show that this
method can be used for unsupervised group-wise image alignment. We show
experiments with expression morphing in humans, hands, and digits, face
manipulation, such as shape and appearance interpolation, as well as
unsupervised landmark localization. A more powerful form of unsupervised
disentangling becomes possible in template coordinates, allowing us to
successfully decompose face images into shading and albedo, and further
manipulate face images.
Conditional Affordance Learning for Driving in Urban Environments
Authors: Axel Sauer, Nikolay Savinov, Andreas Geiger
Date: 2018-06-18 00:00:00
Tasks: Autonomous Driving, Autonomous Navigation, Imitation Learning, Navigate

Most existing approaches to autonomous driving fall into one of two
categories: modular pipelines, that build an extensive model of the
environment, and imitation learning approaches, that map images directly to
control outputs. A recently proposed third paradigm, direct perception, aims to
combine the advantages of both by using a neural network to learn appropriate
low-dimensional intermediate representations. However, existing direct
perception approaches are restricted to simple highway situations, lacking the
ability to navigate intersections, stop at traffic lights or respect speed
limits. In this work, we propose a direct perception approach which maps video
input to intermediate representations suitable for autonomous navigation in
complex urban environments given high-level directional inputs. Compared to
state-of-the-art reinforcement and conditional imitation learning approaches,
we achieve an improvement of up to 68 % in goal-directed navigation on the
challenging CARLA simulation benchmark. In addition, our approach is the first
to handle traffic lights and speed signs by using image-level labels only, as
well as smooth car-following, resulting in a significant reduction of traffic
accidents in simulation.
Power-Grid Controller Anomaly Detection with Enhanced Temporal Deep Learning
Authors: Zecheng He, Aswin Raghavan, Guangyuan Hu, Sek Chai, Ruby Lee
Date: 2018-06-18 00:00:00
Tasks: Anomaly Detection, Deep Learning

Controllers of security-critical cyber-physical systems, like the power grid, are a very important class of computer systems. Attacks against the control code of a power-grid system, especially zero-day attacks, can be catastrophic. Earlier detection of the anomalies can prevent further damage. However, detecting zero-day attacks is extremely challenging because they have no known code and have unknown behavior. Furthermore, if data collected from the controller is transferred to a server through networks for analysis and detection of anomalous behavior, this creates a very large attack surface and also delays detection. In order to address this problem, we propose Reconstruction Error Distribution (RED) of Hardware Performance Counters (HPCs), and a data-driven defense system based on it. Specifically, we first train a temporal deep learning model, using only normal HPC readings from legitimate processes that run daily in these power-grid systems, to model the normal behavior of the power-grid controller. Then, we run this model using real-time data from commonly available HPCs. We use the proposed RED to enhance the temporal deep learning detection of anomalous behavior, by estimating distribution deviations from the normal behavior with an effective statistical test. Experimental results on a real power-grid controller show that we can detect anomalous behavior with high accuracy (>99.9%), nearly zero false positives and short (<360ms) latency.
Women also Snowboard: Overcoming Bias in Captioning Models
Authors: Kaylee Burns, Lisa Anne Hendricks, Kate Saenko, Trevor Darrell, Anna Rohrbach
Date: 2018-03-26 00:00:00
Proceeding: ECCV 2018 9
Tasks: Image Captioning

Most machine learning methods are known to capture and exploit biases of the
training data. While some biases are beneficial for learning, others are
harmful. Specifically, image captioning models tend to exaggerate biases
present in training data (e.g., if a word is present in 60% of training
sentences, it might be predicted in 70% of sentences at test time). This can
lead to incorrect captions in domains where unbiased captions are desired, or
required, due to over-reliance on the learned prior and image context. In this
work we investigate generation of gender-specific caption words (e.g. man,
woman) based on the person's appearance or the image context. We introduce a
new Equalizer model that ensures equal gender probability when gender evidence
is occluded in a scene and confident predictions when gender evidence is
present. The resulting model is forced to look at a person rather than use
contextual cues to make a gender-specific predictions. The losses that comprise
our model, the Appearance Confusion Loss and the Confident Loss, are general,
and can be added to any description model in order to mitigate impacts of
unwanted bias in a description dataset. Our proposed model has lower error than
prior work when describing images with people and mentioning their gender and
more closely matches the ground truth ratio of sentences including women to
sentences including men. We also show that unlike other approaches, our model
is indeed more often looking at people when predicting their gender.
Boosted Density Estimation Remastered
Authors: Zac Cranko, Richard Nock
Date: 2018-03-22 00:00:00
Tasks: Density Estimation, Generative Adversarial Network

There has recently been a steady increase in the number iterative approaches
to density estimation. However, an accompanying burst of formal convergence
guarantees has not followed; all results pay the price of heavy assumptions
which are often unrealistic or hard to check. The Generative Adversarial
Network (GAN) literature --- seemingly orthogonal to the aforementioned pursuit
--- has had the side effect of a renewed interest in variational divergence
minimisation (notably $f$-GAN). We show that by introducing a weak learning
assumption (in the sense of the classical boosting framework) we are able to
import some recent results from the GAN literature to develop an iterative
boosted density estimation algorithm, including formal convergence results with
rates, that does not suffer the shortcomings other approaches. We show that the
density fit is an exponential family, and as part of our analysis obtain an
improved variational characterisation of $f$-GAN.
Disturbance Grassmann Kernels for Subspace-Based Learning
Authors: Junyuan Hong, Huanhuan Chen, Feng Lin
Date: 2018-02-10 00:00:00

In this paper, we focus on subspace-based learning problems, where data
elements are linear subspaces instead of vectors. To handle this kind of data,
Grassmann kernels were proposed to measure the space structure and used with
classifiers, e.g., Support Vector Machines (SVMs). However, the existing
discriminative algorithms mostly ignore the instability of subspaces, which
would cause the classifiers misled by disturbed instances. Thus we propose
considering all potential disturbance of subspaces in learning processes to
obtain more robust classifiers. Firstly, we derive the dual optimization of
linear classifiers with disturbance subject to a known distribution, resulting
in a new kernel, Disturbance Grassmann (DG) kernel. Secondly, we research into
two kinds of disturbance, relevant to the subspace matrix and singular values
of bases, with which we extend the Projection kernel on Grassmann manifolds to
two new kernels. Experiments on action data indicate that the proposed kernels
perform better compared to state-of-the-art subspace-based methods, even in a
worse environment.