pwc-archive/papers-with-abstracts huggingface dataset:
A Dataset for Developing and Benchmarking Active Vision
Authors: Phil Ammirato, Patrick Poirson, Eunbyung Park, Jana Kosecka, Alexander C. Berg
Date: 2017-02-27 00:00:00
Tasks: Benchmarking, General Classification, Object, object-detection, Object Detection, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present a new public dataset with a focus on simulating robotic vision
tasks in everyday indoor environments using real imagery. The dataset includes
20,000+ RGB-D images and 50,000+ 2D bounding boxes of object instances densely
captured in 9 unique scenes. We train a fast object category detector for
instance detection on our data. Using the dataset we show that, although
increasingly accurate and fast, the state of the art for object detection is
still severely impacted by object scale, occlusion, and viewing direction all
of which matter for robotics applications. We next validate the dataset for
simulating active vision, and use the dataset to develop and evaluate a
deep-network-based system for next best move prediction for object
classification using reinforcement learning. Our dataset is available for
download at cs.unc.edu/~ammirato/active_vision_dataset_website/.
EPOpt: Learning Robust Neural Network Policies Using Model Ensembles
Authors: Aravind Rajeswaran, Sarvjeet Ghotra, Balaraman Ravindran, Sergey Levine
Date: 2016-10-05 00:00:00
Tasks: Domain Adaptation, Reinforcement Learning

Sample complexity and safety are major challenges when learning policies with
reinforcement learning for real-world tasks, especially when the policies are
represented using rich function approximators like deep neural networks.
Model-based methods where the real-world target domain is approximated using a
simulated source domain provide an avenue to tackle the above challenges by
augmenting real data with simulated data. However, discrepancies between the
simulated source domain and the target domain pose a challenge for simulated
training. We introduce the EPOpt algorithm, which uses an ensemble of simulated
source domains and a form of adversarial training to learn policies that are
robust and generalize to a broad range of possible target domains, including
unmodeled effects. Further, the probability distribution over source domains in
the ensemble can be adapted using data from target domain and approximate
Bayesian methods, to progressively make it a better approximation. Thus,
learning on a model ensemble, along with source domain adaptation, provides the
benefit of both robustness and learning/adaptation.
Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data
Authors: Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian Goodfellow, Kunal Talwar
Date: 2016-10-18 00:00:00
Tasks: Transfer Learning

Some machine learning applications involve training data that is sensitive,
such as the medical histories of patients in a clinical trial. A model may
inadvertently and implicitly store some of its training data; careful analysis
of the model may therefore reveal sensitive information.
  To address this problem, we demonstrate a generally applicable approach to
providing strong privacy guarantees for training data: Private Aggregation of
Teacher Ensembles (PATE). The approach combines, in a black-box fashion,
multiple models trained with disjoint datasets, such as records from different
subsets of users. Because they rely directly on sensitive data, these models
are not published, but instead used as "teachers" for a "student" model. The
student learns to predict an output chosen by noisy voting among all of the
teachers, and cannot directly access an individual teacher or the underlying
data or parameters. The student's privacy properties can be understood both
intuitively (since no single teacher and thus no single dataset dictates the
student's training) and formally, in terms of differential privacy. These
properties hold even if an adversary can not only query the student but also
inspect its internal workings.
  Compared with previous work, the approach imposes only weak assumptions on
how teachers are trained: it applies to any model, including non-convex models
like DNNs. We achieve state-of-the-art privacy/utility trade-offs on MNIST and
SVHN thanks to an improved privacy analysis and semi-supervised learning.
Bridging Saliency Detection to Weakly Supervised Object Detection Based on Self-paced Curriculum Learning
Authors: Dingwen Zhang, Deyu Meng, Long Zhao, Junwei Han
Date: 2017-03-03 00:00:00
Tasks: Object, object-detection, Object Detection, Saliency Detection, Weakly Supervised Object Detection

Weakly-supervised object detection (WOD) is a challenging problems in
computer vision. The key problem is to simultaneously infer the exact object
locations in the training images and train the object detectors, given only the
training images with weak image-level labels. Intuitively, by simulating the
selective attention mechanism of human visual system, saliency detection
technique can select attractive objects in scenes and thus is a potential way
to provide useful priors for WOD. However, the way to adopt saliency detection
in WOD is not trivial since the detected saliency region might be possibly
highly ambiguous in complex cases. To this end, this paper first
comprehensively analyzes the challenges in applying saliency detection to WOD.
Then, we make one of the earliest efforts to bridge saliency detection to WOD
via the self-paced curriculum learning, which can guide the learning procedure
to gradually achieve faithful knowledge of multi-class objects from easy to
hard. The experimental results demonstrate that the proposed approach can
successfully bridge saliency detection and WOD tasks and achieve the
state-of-the-art object detection results under the weak supervision.
Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data
Authors: Maximilian Karl, Maximilian Soelch, Justin Bayer, Patrick van der Smagt
Date: 2016-05-20 00:00:00
Tasks: State Space Models, Variational Inference

We introduce Deep Variational Bayes Filters (DVBF), a new method for
unsupervised learning and identification of latent Markovian state space
models. Leveraging recent advances in Stochastic Gradient Variational Bayes,
DVBF can overcome intractable inference distributions via variational
inference. Thus, it can handle highly nonlinear input data with temporal and
spatial dependencies such as image sequences without domain knowledge. Our
experiments show that enabling backpropagation through transitions enforces
state space assumptions and significantly improves information content of the
latent embedding. This also enables realistic long-term prediction.
Capacity and Trainability in Recurrent Neural Networks
Authors: Jasmine Collins, Jascha Sohl-Dickstein, David Sussillo
Date: 2016-11-29 00:00:00

Two potential bottlenecks on the expressiveness of recurrent neural networks
(RNNs) are their ability to store information about the task in their
parameters, and to store information about the input history in their units. We
show experimentally that all common RNN architectures achieve nearly the same
per-task and per-unit capacity bounds with careful training, for a variety of
tasks and stacking depths. They can store an amount of task information which
is linear in the number of parameters, and is approximately 5 bits per
parameter. They can additionally store approximately one real number from their
input history per hidden unit. We further find that for several tasks it is the
per-task parameter capacity bound that determines performance. These results
suggest that many previous results comparing RNN architectures are driven
primarily by differences in training effectiveness, rather than differences in
capacity. Supporting this observation, we compare training difficulty for
several architectures, and show that vanilla RNNs are far more difficult to
train, yet have slightly higher capacity. Finally, we propose two novel RNN
architectures, one of which is easier to train than the LSTM or GRU for deeply
stacked architectures.
Virtual vs. Real: Trading Off Simulations and Physical Experiments in Reinforcement Learning with Bayesian Optimization
Authors: Alonso Marco, Felix Berkenkamp, Philipp Hennig, Angela P. Schoellig, Andreas Krause, Stefan Schaal, Sebastian Trimpe
Date: 2017-03-03 00:00:00
Tasks: Bayesian Optimization, Reinforcement Learning

In practice, the parameters of control policies are often tuned manually.
This is time-consuming and frustrating. Reinforcement learning is a promising
alternative that aims to automate this process, yet often requires too many
experiments to be practical. In this paper, we propose a solution to this
problem by exploiting prior knowledge from simulations, which are readily
available for most robotic platforms. Specifically, we extend Entropy Search, a
Bayesian optimization algorithm that maximizes information gain from each
experiment, to the case of multiple information sources. The result is a
principled way to automatically combine cheap, but inaccurate information from
simulations with expensive and accurate physical experiments in a
cost-effective manner. We apply the resulting method to a cart-pole system,
which confirms that the algorithm can find good control policies with fewer
experiments than standard Bayesian optimization on the physical system only.
Incident Light Frequency-based Image Defogging Algorithm
Authors: Wenbo Zhang, Xiaorong Hou
Date: 2017-03-03 00:00:00
Tasks: Image Restoration

Considering the problem of color distortion caused by the defogging algorithm
based on dark channel prior, an improved algorithm was proposed to calculate
the transmittance of all channels respectively. First, incident light
frequency's effect on the transmittance of various color channels was analyzed
according to the Beer-Lambert's Law, from which a proportion among various
channel transmittances was derived; afterwards, images were preprocessed by
down-sampling to refine transmittance, and then the original size was restored
to enhance the operational efficiency of the algorithm; finally, the
transmittance of all color channels was acquired in accordance with the
proportion, and then the corresponding transmittance was used for image
restoration in each channel. The experimental results show that compared with
the existing algorithm, this improved image defogging algorithm could make
image colors more natural, solve the problem of slightly higher color
saturation caused by the existing algorithm, and shorten the operation time by
four to nine times.
A Bayesian computer model analysis of Robust Bayesian analyses
Authors: Ian Vernon, John Paul Gosling
Date: 2017-03-03 00:00:00

We harness the power of Bayesian emulation techniques, designed to aid the
analysis of complex computer models, to examine the structure of complex
Bayesian analyses themselves. These techniques facilitate robust Bayesian
analyses and/or sensitivity analyses of complex problems, and hence allow
global exploration of the impacts of choices made in both the likelihood and
prior specification. We show how previously intractable problems in robustness
studies can be overcome using emulation techniques, and how these methods allow
other scientists to quickly extract approximations to posterior results
corresponding to their own particular subjective specification. The utility and
flexibility of our method is demonstrated on a reanalysis of a real application
where Bayesian methods were employed to capture beliefs about river flow. We
discuss the obvious extensions and directions of future research that such an
approach opens up.
Deep Collaborative Learning for Visual Recognition
Authors: Yan Wang, Lingxi Xie, Ya zhang, Wenjun Zhang, Alan Yuille
Date: 2017-03-03 00:00:00
Tasks: General Classification, image-classification, Image Classification

Deep neural networks are playing an important role in state-of-the-art visual
recognition. To represent high-level visual concepts, modern networks are
equipped with large convolutional layers, which use a large number of filters
and contribute significantly to model complexity. For example, more than half
of the weights of AlexNet are stored in the first fully-connected layer (4,096
filters).
  We formulate the function of a convolutional layer as learning a large visual
vocabulary, and propose an alternative way, namely Deep Collaborative Learning
(DCL), to reduce the computational complexity. We replace a convolutional layer
with a two-stage DCL module, in which we first construct a couple of smaller
convolutional layers individually, and then fuse them at each spatial position
to consider feature co-occurrence. In mathematics, DCL can be explained as an
efficient way of learning compositional visual concepts, in which the
vocabulary size increases exponentially while the model complexity only
increases linearly. We evaluate DCL on a wide range of visual recognition
tasks, including a series of multi-digit number classification datasets, and
some generic image classification datasets such as SVHN, CIFAR and ILSVRC2012.
We apply DCL to several state-of-the-art network structures, improving the
recognition accuracy meanwhile reducing the number of parameters (16.82% fewer
in AlexNet).
Context Aware Query Image Representation for Particular Object Retrieval
Authors: Zakaria Laskar, Juho Kannala
Date: 2017-03-03 00:00:00
Tasks: Image Retrieval, Retrieval

The current models of image representation based on Convolutional Neural
Networks (CNN) have shown tremendous performance in image retrieval. Such
models are inspired by the information flow along the visual pathway in the
human visual cortex. We propose that in the field of particular object
retrieval, the process of extracting CNN representations from query images with
a given region of interest (ROI) can also be modelled by taking inspiration
from human vision. Particularly, we show that by making the CNN pay attention
on the ROI while extracting query image representation leads to significant
improvement over the baseline methods on challenging Oxford5k and Paris6k
datasets. Furthermore, we propose an extension to a recently introduced
encoding method for CNN representations, regional maximum activations of
convolutions (R-MAC). The proposed extension weights the regional
representations using a novel saliency measure prior to aggregation. This leads
to further improvement in retrieval accuracy.
Learning Graphical Games from Behavioral Data: Sufficient and Necessary Conditions
Authors: Asish Ghoshal, Jean Honorio
Date: 2017-03-03 00:00:00

In this paper we obtain sufficient and necessary conditions on the number of
samples required for exact recovery of the pure-strategy Nash equilibria (PSNE)
set of a graphical game from noisy observations of joint actions. We consider
sparse linear influence games --- a parametric class of graphical games with
linear payoffs, and represented by directed graphs of n nodes (players) and
in-degree of at most k. We show that one can efficiently recover the PSNE set
of a linear influence game with $O(k^2 \log n)$ samples, under very general
observation models. On the other hand, we show that $\Omega(k \log n)$ samples
are necessary for any procedure to recover the PSNE set from observations of
joint actions.
An Actor-Critic Algorithm for Sequence Prediction
Authors: Dzmitry Bahdanau, Philemon Brakel, Kelvin Xu, Anirudh Goyal, Ryan Lowe, Joelle Pineau, Aaron Courville, Yoshua Bengio
Date: 2016-07-24 00:00:00
Tasks: Caption Generation, Machine Translation, Prediction, Reinforcement Learning, Reinforcement Learning (RL), Spelling Correction, Text Generation, Translation

We present an approach to training neural networks to generate sequences
using actor-critic methods from reinforcement learning (RL). Current
log-likelihood training methods are limited by the discrepancy between their
training and testing modes, as models must generate tokens conditioned on their
previous guesses rather than the ground-truth tokens. We address this problem
by introducing a \textit{critic} network that is trained to predict the value
of an output token, given the policy of an \textit{actor} network. This results
in a training procedure that is much closer to the test phase, and allows us to
directly optimize for a task-specific score such as BLEU. Crucially, since we
leverage these techniques in the supervised learning setting rather than the
traditional RL setting, we condition the critic network on the ground-truth
output. We show that our method leads to improved performance on both a
synthetic task, and for German-English machine translation. Our analysis paves
the way for such methods to be applied in natural language generation tasks,
such as machine translation, caption generation, and dialogue modelling.
EmotioNet Challenge: Recognition of facial expressions of emotion in the wild
Authors: C. Fabian Benitez-Quiroz, Ramprakash Srinivasan, Qianli Feng, Yan Wang, Aleix M. Martinez
Date: 2017-03-03 00:00:00

This paper details the methodology and results of the EmotioNet challenge.
This challenge is the first to test the ability of computer vision algorithms
in the automatic analysis of a large number of images of facial expressions of
emotion in the wild. The challenge was divided into two tracks. The first track
tested the ability of current computer vision algorithms in the automatic
detection of action units (AUs). Specifically, we tested the detection of 11
AUs. The second track tested the algorithms' ability to recognize emotion
categories in images of facial expressions. Specifically, we tested the
recognition of 16 basic and compound emotion categories. The results of the
challenge suggest that current computer vision and machine learning algorithms
are unable to reliably solve these two tasks. The limitations of current
algorithms are more apparent when trying to recognize emotion. We also show
that current algorithms are not affected by mild resolution changes, small
occluders, gender or age, but that 3D pose is a major limiting factor on
performance. We provide an in-depth discussion of the points that need special
attention moving forward.
Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity
Authors: Asish Ghoshal, Jean Honorio
Date: 2017-03-03 00:00:00
Proceeding: NeurIPS 2017 12

Learning the directed acyclic graph (DAG) structure of a Bayesian network
from observational data is a notoriously difficult problem for which many
hardness results are known. In this paper we propose a provably polynomial-time
algorithm for learning sparse Gaussian Bayesian networks with equal noise
variance --- a class of Bayesian networks for which the DAG structure can be
uniquely identified from observational data --- under high-dimensional
settings. We show that $O(k^4 \log p)$ number of samples suffices for our
method to recover the true DAG structure with high probability, where $p$ is
the number of variables and $k$ is the maximum Markov blanket size. We obtain
our theoretical guarantees under a condition called Restricted Strong Adjacency
Faithfulness, which is strictly weaker than strong faithfulness --- a condition
that other methods based on conditional independence testing need for their
success. The sample complexity of our method matches the information-theoretic
limits in terms of the dependence on $p$. We show that our method out-performs
existing state-of-the-art methods for learning Gaussian Bayesian networks in
terms of recovering the true DAG structure while being comparable in speed to
heuristic methods.
End-to-end Optimized Image Compression
Authors: Johannes Ballé, Valero Laparra, Eero P. Simoncelli
Date: 2016-11-05 00:00:00
Tasks: Image Compression, MS-SSIM, SSIM

We describe an image compression method, consisting of a nonlinear analysis
transformation, a uniform quantizer, and a nonlinear synthesis transformation.
The transforms are constructed in three successive stages of convolutional
linear filters and nonlinear activation functions. Unlike most convolutional
neural networks, the joint nonlinearity is chosen to implement a form of local
gain control, inspired by those used to model biological neurons. Using a
variant of stochastic gradient descent, we jointly optimize the entire model
for rate-distortion performance over a database of training images, introducing
a continuous proxy for the discontinuous loss function arising from the
quantizer. Under certain conditions, the relaxed loss function may be
interpreted as the log likelihood of a generative model, as implemented by a
variational autoencoder. Unlike these models, however, the compression model
must operate at any given point along the rate-distortion curve, as specified
by a trade-off parameter. Across an independent set of test images, we find
that the optimized method generally exhibits better rate-distortion performance
than the standard JPEG and JPEG 2000 compression methods. More importantly, we
observe a dramatic improvement in visual quality for all images at all bit
rates, which is supported by objective quality estimates using MS-SSIM.
A Survey on Content-Aware Video Analysis for Sports
Authors: Huang-Chia Shih
Date: 2017-03-03 00:00:00
Tasks: Survey

Sports data analysis is becoming increasingly large-scale, diversified, and
shared, but difficulty persists in rapidly accessing the most crucial
information. Previous surveys have focused on the methodologies of sports video
analysis from the spatiotemporal viewpoint instead of a content-based
viewpoint, and few of these studies have considered semantics. This study
develops a deeper interpretation of content-aware sports video analysis by
examining the insight offered by research into the structure of content under
different scenarios. On the basis of this insight, we provide an overview of
the themes particularly relevant to the research on content-aware systems for
broadcast sports. Specifically, we focus on the video content analysis
techniques applied in sportscasts over the past decade from the perspectives of
fundamentals and general review, a content hierarchical model, and trends and
challenges. Content-aware analysis methods are discussed with respect to
object-, event-, and context-oriented groups. In each group, the gap between
sensation and content excitement must be bridged using proper strategies. In
this regard, a content-aware approach is required to determine user demands.
Finally, the paper summarizes the future trends and challenges for sports video
analysis. We believe that our findings can advance the field of research on
content-aware video analysis for broadcast sports.
Progressive Attention Networks for Visual Attribute Prediction
Authors: Paul Hongsuck Seo, Zhe Lin, Scott Cohen, Xiaohui Shen, Bohyung Han
Date: 2016-06-08 00:00:00
Tasks: Attribute, Hard Attention, Prediction

We propose a novel attention model that can accurately attends to target
objects of various scales and shapes in images. The model is trained to
gradually suppress irrelevant regions in an input image via a progressive
attentive process over multiple layers of a convolutional neural network. The
attentive process in each layer determines whether to pass or block features at
certain spatial locations for use in the subsequent layers. The proposed
progressive attention mechanism works well especially when combined with hard
attention. We further employ local contexts to incorporate neighborhood
features of each location and estimate a better attention probability map. The
experiments on synthetic and real datasets show that the proposed attention
networks outperform traditional attention methods in visual attribute
prediction tasks.