pwc-archive/papers-with-abstracts huggingface dataset:
Scalable Angular Discriminative Deep Metric Learning for Face Recognition
Authors: Bowen Wu, Huaming Wu, Monica M. Y. Zhang
Date: 2018-04-29 00:00:00
Tasks: Face Recognition, Metric Learning

With the development of deep learning, Deep Metric Learning (DML) has
achieved great improvements in face recognition. Specifically, the widely used
softmax loss in the training process often bring large intra-class variations,
and feature normalization is only exploited in the testing process to compute
the pair similarities. To bridge the gap, we impose the intra-class cosine
similarity between the features and weight vectors in softmax loss larger than
a margin in the training step, and extend it from four aspects. First, we
explore the effect of a hard sample mining strategy. To alleviate the human
labor of adjusting the margin hyper-parameter, a self-adaptive margin updating
strategy is proposed. Then, a normalized version is given to take full
advantage of the cosine similarity constraint. Furthermore, we enhance the
former constraint to force the intra-class cosine similarity larger than the
mean inter-class cosine similarity with a margin in the exponential feature
projection space. Extensive experiments on Labeled Face in the Wild (LFW),
Youtube Faces (YTF) and IARPA Janus Benchmark A (IJB-A) datasets demonstrate
that the proposed methods outperform the mainstream DML methods and approach
the state-of-the-art performance.
Dialog-based Interactive Image Retrieval
Authors: Xiaoxiao Guo, Hui Wu, Yu Cheng, Steven Rennie, Gerald Tesauro, Rogerio Schmidt Feris
Date: 2018-05-01 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Image Retrieval, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Retrieval, Visual Dialog

Existing methods for interactive image retrieval have demonstrated the merit
of integrating user feedback, improving retrieval results. However, most
current systems rely on restricted forms of user feedback, such as binary
relevance responses, or feedback based on a fixed set of relative attributes,
which limits their impact. In this paper, we introduce a new approach to
interactive image search that enables users to provide feedback via natural
language, allowing for more natural and effective interaction. We formulate the
task of dialog-based interactive image retrieval as a reinforcement learning
problem, and reward the dialog system for improving the rank of the target
image during each dialog turn. To mitigate the cumbersome and costly process of
collecting human-machine conversations as the dialog system learns, we train
our system with a user simulator, which is itself trained to describe the
differences between target and candidate images. The efficacy of our approach
is demonstrated in a footwear retrieval application. Experiments on both
simulated and real-world data show that 1) our proposed learning framework
achieves better accuracy than other supervised and reinforcement learning
baselines and 2) user feedback based on natural language rather than
pre-specified attributes leads to more effective retrieval results, and a more
natural and expressive communication interface.
Deep Barcodes for Fast Retrieval of Histopathology Scans
Authors: Meghana Dinesh Kumar, Morteza Babaie, Hamid Tizhoosh
Date: 2018-04-30 00:00:00
Tasks: General Classification, Image Retrieval, Retrieval

We investigate the concept of deep barcodes and propose two methods to
generate them in order to expedite the process of classification and retrieval
of histopathology images. Since binary search is computationally less
expensive, in terms of both speed and storage, deep barcodes could be useful
when dealing with big data retrieval. Our experiments use the dataset Kimia
Path24 to test three pre-trained networks for image retrieval. The dataset
consists of 27,055 training images in 24 different classes with large
variability, and 1,325 test images for testing. Apart from the high-speed and
efficiency, results show a surprising retrieval accuracy of 71.62% for deep
barcodes, as compared to 68.91% for deep features and 68.53% for compressed
deep features.
The Effects of Image Pre- and Post-Processing, Wavelet Decomposition, and Local Binary Patterns on U-Nets for Skin Lesion Segmentation
Authors: Sara Ross-Howe, H. R. Tizhoosh
Date: 2018-04-30 00:00:00
Tasks: Lesion Segmentation, Skin Lesion Segmentation

Skin cancer is a widespread, global, and potentially deadly disease, which
over the last three decades has afflicted more lives in the USA than all other
forms of cancer combined. There have been a lot of promising recent works
utilizing deep network architectures, such as FCNs, U-Nets, and ResNets, for
developing automated skin lesion segmentation. This paper investigates various
pre- and post-processing techniques for improving the performance of U-Nets as
measured by the Jaccard Index. The dataset provided as part of the "2017 ISBI
Challenges on Skin Lesion Analysis Towards Melanoma Detection" was used for
this evaluation and the performance of the finalist competitors was the
standard for comparison. The pre-processing techniques employed in the proposed
system included contrast enhancement, artifact removal, and vignette
correction. More advanced image transformations, such as local binary patterns
and wavelet decomposition, were also employed to augment the raw grayscale
images used as network input features. While the performance of the proposed
system fell short of the winners of the challenge, it was determined that using
wavelet decomposition as an early transformation step improved the overall
performance of the system over pre- and post-processing steps alone.
Weighted Bilinear Coding over Salient Body Parts for Person Re-identification
Authors: Zhigang Chang, Qin Zhou, Heng Fan, Hang Su, Hua Yang, Shibao Zheng, Haibin Ling
Date: 2018-03-22 00:00:00
Tasks: Person Re-Identification

Deep convolutional neural networks (CNNs) have demonstrated dominant performance in person re-identification (Re-ID). Existing CNN based methods utilize global average pooling (GAP) to aggregate intermediate convolutional features for Re-ID. However, this strategy only considers the first-order statistics of local features and treats local features at different locations equally important, leading to sub-optimal feature representation. To deal with these issues, we propose a novel weighted bilinear coding (WBC) framework for local feature aggregation in CNN networks to pursue more representative and discriminative feature representations, which can adapt to other state-of-the-art methods and improve their performance. In specific, bilinear coding is used to encode the channel-wise feature correlations to capture richer feature interactions. Meanwhile, a weighting scheme is applied on the bilinear coding to adaptively adjust the weights of local features at different locations based on their importance in recognition, further improving the discriminability of feature aggregation. To handle the spatial misalignment issue, we use a salient part net (spatial attention module) to derive salient body parts, and apply the WBC model on each part. The final representation, formed by concatenating the WBC encoded features of each part, is both discriminative and resistant to spatial misalignment. Experiments on three benchmarks including Market-1501, DukeMTMC-reID and CUHK03 evidence the favorable performance of our method against other outstanding methods.
CrowdHuman: A Benchmark for Detecting Human in a Crowd
Authors: Shuai Shao, Zijian Zhao, Boxun Li, Tete Xiao, Gang Yu, Xiangyu Zhang, Jian Sun
Date: 2018-04-30 00:00:00
Tasks: Diversity, Human Detection, Object Detection, Pedestrian Detection

Human detection has witnessed impressive progress in recent years. However,
the occlusion issue of detecting human in highly crowded environments is far
from solved. To make matters worse, crowd scenarios are still under-represented
in current human detection benchmarks. In this paper, we introduce a new
dataset, called CrowdHuman, to better evaluate detectors in crowd scenarios.
The CrowdHuman dataset is large, rich-annotated and contains high diversity.
There are a total of $470K$ human instances from the train and validation
subsets, and $~22.6$ persons per image, with various kinds of occlusions in the
dataset. Each human instance is annotated with a head bounding-box, human
visible-region bounding-box and human full-body bounding-box. Baseline
performance of state-of-the-art detection frameworks on CrowdHuman is
presented. The cross-dataset generalization results of CrowdHuman dataset
demonstrate state-of-the-art performance on previous dataset including
Caltech-USA, CityPersons, and Brainwash without bells and whistles. We hope our
dataset will serve as a solid baseline and help promote future research in
human detection tasks.
A Missing Information Loss function for implicit feedback datasets
Authors: Juan Arévalo, Juan Ramón Duque, Marco Creatura
Date: 2018-04-30 00:00:00
Tasks: Denoising, Recommendation Systems, valid

Latent factor models for Recommender Systems with implicit feedback typically
treat unobserved user-item interactions (i.e. missing information) as negative
feedback. This is frequently done either through negative sampling (point--wise
loss) or with a ranking loss function (pair-- or list--wise estimation). Since
a zero preference recommendation is a valid solution for most common objective
functions, regarding unknown values as actual zeros results in users having a
zero preference recommendation for most of the available items. In this paper
we propose a novel objective function, the \emph{Missing Information Loss}
(MIL), that explicitly forbids treating unobserved user-item interactions as
positive or negative feedback. We apply this loss to both traditional Matrix
Factorization and user--based Denoising Autoencoder, and compare it with other
established objective functions such as cross-entropy (both point- and
pair-wise) or the recently proposed multinomial log-likelihood. MIL achieves
competitive performance in ranking-aware metrics when applied to three
datasets. Furthermore, we show that such a relevance in the recommendation is
obtained while displaying popular items less frequently (up to a $20 \%$
decrease with respect to the best competing method). This debiasing from the
recommendation of popular items favours the appearance of infrequent items (up
to a $50 \%$ increase of long-tail recommendations), a valuable feature for
Recommender Systems with a large catalogue of products.
Risk-Averse Classification
Authors: Constantine Vitt, Darinka Dentcheva, Hui Xiong
Date: 2018-04-30 00:00:00
Tasks: Binary Classification, Classification, General Classification

We develop a new approach to solving classification problems, which is bases
on the theory of coherent measures of risk and risk sharing ideas. The proposed
approach aims at designing a risk-averse classifier. The new approach allows
for associating distinct risk functional to each classes. The risk may be
measured by different (non-linear in probability) measures,
  We analyze the structure of the new classifier design problem and establish
its theoretical relation to known risk-neutral design problems. In particular,
we show that the risk-sharing classification problem is equivalent to an
implicitly defined optimization problem with unequal, implicitly defined but
unknown, weights for each data point. We implement our methodology in a binary
classification scenario on several different data sets and carry out numerical
comparison with classifiers which are obtained using the Huber loss function
and other loss functions known in the literature. We formulate specific
risk-averse support vector machines in order to demonstrate the viability of
our method.
Quantum generative adversarial networks
Authors: Pierre-Luc Dallaire-Demers, Nathan Killoran
Date: 2018-04-23 00:00:00
Tasks: BIG-bench Machine Learning, Generative Adversarial Network, Quantum Machine Learning

Quantum machine learning is expected to be one of the first potential
general-purpose applications of near-term quantum devices. A major recent
breakthrough in classical machine learning is the notion of generative
adversarial training, where the gradients of a discriminator model are used to
train a separate generative model. In this work and a companion paper, we
extend adversarial training to the quantum domain and show how to construct
generative adversarial networks using quantum circuits. Furthermore, we also
show how to compute gradients -- a key element in generative adversarial
network training -- using another quantum circuit. We give an example of a
simple practical circuit ansatz to parametrize quantum machine learning models
and perform a simple numerical experiment to demonstrate that quantum
generative adversarial networks can be trained successfully.
A Canonical Image Set for Examining and Comparing Image Processing Algorithms
Authors: Jeffrey Uhlmann
Date: 2018-04-30 00:00:00
Tasks: Image Compression

The purpose of this paper is to introduce a set of four test images
containing features and structures that can facilitate effective examination
and comparison of image processing algorithms. More specifically, the images
are designed to more explicitly expose the characteristic properties of
algorithms for image compression, virtual resolution adjustment, and
enhancement. This set was developed at the Naval Research Laboratory (NRL) in
the late 1990s as a more rigorous alternative to Lena and other images that
have come into common use for purely ad hoc reasons with little or no rigorous
consideration of their suitability. The increasing number of test images
appearing in the literature not only makes it more difficult to compare results
from different papers, it also introduces the potential for cherry-picking to
influence results. The key contribution of this paper is the proposal to
establish {\em some} canonical set to ensure that published results can be
analyzed and compared in a rigorous way from one paper to another, and
consideration of the four NRL images is proposed for this purpose.
Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
Authors: Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, Kaiming He
Date: 2017-06-08 00:00:00
Tasks: Stochastic Optimization

Deep learning thrives with large neural networks and large datasets. However,
larger networks and larger datasets result in longer training times that impede
research and development progress. Distributed synchronous SGD offers a
potential solution to this problem by dividing SGD minibatches over a pool of
parallel workers. Yet to make this scheme efficient, the per-worker workload
must be large, which implies nontrivial growth in the SGD minibatch size. In
this paper, we empirically show that on the ImageNet dataset large minibatches
cause optimization difficulties, but when these are addressed the trained
networks exhibit good generalization. Specifically, we show no loss of accuracy
when training with large minibatch sizes up to 8192 images. To achieve this
result, we adopt a hyper-parameter-free linear scaling rule for adjusting
learning rates as a function of minibatch size and develop a new warmup scheme
that overcomes optimization challenges early in training. With these simple
techniques, our Caffe2-based system trains ResNet-50 with a minibatch size of
8192 on 256 GPUs in one hour, while matching small minibatch accuracy. Using
commodity hardware, our implementation achieves ~90% scaling efficiency when
moving from 8 to 256 GPUs. Our findings enable training visual recognition
models on internet-scale data with high efficiency.
Conditional molecular design with deep generative models
Authors: Seokho Kang, Kyunghyun Cho
Date: 2018-04-30 00:00:00
Tasks: Property Prediction

Although machine learning has been successfully used to propose novel
molecules that satisfy desired properties, it is still challenging to explore a
large chemical space efficiently. In this paper, we present a conditional
molecular design method that facilitates generating new molecules with desired
properties. The proposed model, which simultaneously performs both property
prediction and molecule generation, is built as a semi-supervised variational
autoencoder trained on a set of existing molecules with only a partial
annotation. We generate new molecules with desired properties by sampling from
the generative distribution estimated by the model. We demonstrate the
effectiveness of the proposed model by evaluating it on drug-like molecules.
The model improves the performance of property prediction by exploiting
unlabeled molecules, and efficiently generates novel molecules fulfilling
various target conditions.
Faster Subgradient Methods for Functions with Hölderian Growth
Authors: Patrick R. Johnstone, Pierre Moulin
Date: 2017-04-01 00:00:00

The purpose of this manuscript is to derive new convergence results for
several subgradient methods applied to minimizing nonsmooth convex functions
with H\"olderian growth. The growth condition is satisfied in many applications
and includes functions with quadratic growth and weakly sharp minima as special
cases. To this end there are three main contributions. First, for a constant
and sufficiently small stepsize, we show that the subgradient method achieves
linear convergence up to a certain region including the optimal set, with error
of the order of the stepsize. Second, if appropriate problem parameters are
known, we derive a decaying stepsize which obtains a much faster convergence
rate than is suggested by the classical $O(1/\sqrt{k})$ result for the
subgradient method. Thirdly we develop a novel "descending stairs" stepsize
which obtains this faster convergence rate and also obtains linear convergence
for the special case of weakly sharp functions. We also develop an adaptive
variant of the "descending stairs" stepsize which achieves the same convergence
rate without requiring an error bound constant which is difficult to estimate
in practice.
Particle-based pedestrian path prediction using LSTM-MDL models
Authors: Ronny Hug, Stefan Becker, Wolfgang Hübner, Michael Arens
Date: 2018-04-16 00:00:00

Recurrent neural networks are able to learn complex long-term relationships
from sequential data and output a pdf over the state space. Therefore,
recurrent models are a natural choice to address path prediction tasks, where a
trained model is used to generate future expectations from past observations.
When applied to security applications, like predicting the path of pedestrians
for risk assessment, a point-wise greedy (ML) evaluation of the output pdf is
not feasible, since the environment often allows multiple choices. Therefore, a
robust risk assessment has to take all options into account, even if they are
overall not very likely.
  Towards this end, a combination of particle filter sampling strategies and a
LSTM-MDL model is proposed to address a multi-modal path prediction task. The
capabilities and viability of the proposed approach are evaluated on several
synthetic test conditions, yielding the counter-intuitive result that the
simplest approach performs best. Further, the feasibility of the proposed
approach is illustrated on several real world scenes.
Syntactic Patterns Improve Information Extraction for Medical Search
Authors: Roma Patel, Yinfei Yang, Iain Marshall, Ani Nenkova, Byron Wallace
Date: 2018-04-30 00:00:00
Proceeding: NAACL 2018 6

Medical professionals search the published literature by specifying the type
of patients, the medical intervention(s) and the outcome measure(s) of
interest. In this paper we demonstrate how features encoding syntactic patterns
improve the performance of state-of-the-art sequence tagging models (both
linear and neural) for information extraction of these medically relevant
categories. We present an analysis of the type of patterns exploited, and the
semantic space induced for these, i.e., the distributed representations learned
for identified multi-token patterns. We show that these learned representations
differ substantially from those of the constituent unigrams, suggesting that
the patterns capture contextual information that is otherwise lost.
New Methods of Studying Valley Fitness Landscapes
Authors: Jun He, Tao Xu
Date: 2018-04-30 00:00:00

The word "valley" is a popular term used in intuitively describing fitness
landscapes. What is a valley on a fitness landscape? How to identify the
direction and location of a valley if it exists? However, such questions are
seldom rigorously studied in evolutionary optimization especially when the
search space is a high dimensional continuous space. This paper presents two
methods of studying valleys on a fitness landscape. The first method is based
on the topological homeomorphism. It establishes a rigorous definition of a
valley. A valley is regarded as a one-dimensional manifold. The second method
takes a different viewpoint from statistics. It provides an algorithm of
identifying the valley direction and location using principle component
analysis.
Concolic Testing for Deep Neural Networks
Authors: Youcheng Sun, Min Wu, Wenjie Ruan, Xiaowei Huang, Marta Kwiatkowska, Daniel Kroening
Date: 2018-04-30 00:00:00

Concolic testing combines program execution and symbolic analysis to explore
the execution paths of a software program. This paper presents the first
concolic testing approach for Deep Neural Networks (DNNs). More specifically,
we formalise coverage criteria for DNNs that have been studied in the
literature, and then develop a coherent method for performing concolic testing
to increase test coverage. Our experimental results show the effectiveness of
the concolic testing approach in both achieving high coverage and finding
adversarial examples.
Tags2Parts: Discovering Semantic Regions from Shape Tags
Authors: Sanjeev Muralikrishnan, Vladimir G. Kim, Siddhartha Chaudhuri
Date: 2017-08-22 00:00:00
Proceeding: CVPR 2018 6
Tasks: TAG

We propose a novel method for discovering shape regions that strongly
correlate with user-prescribed tags. For example, given a collection of chairs
tagged as either "has armrest" or "lacks armrest", our system correctly
highlights the armrest regions as the main distinctive parts between the two
chair types. To obtain point-wise predictions from shape-wise tags we develop a
novel neural network architecture that is trained with tag classification loss,
but is designed to rely on segmentation to predict the tag. Our network is
inspired by U-Net, but we replicate shallow U structures several times with new
skip connections and pooling layers, and call the resulting architecture
"WU-Net". We test our method on segmentation benchmarks and show that even with
weak supervision of whole shape tags, our method can infer meaningful semantic
regions, without ever observing shape segmentations. Further, once trained, the
model can process shapes for which the tag is entirely unknown. As a bonus, our
architecture is directly operational under full supervision and performs
strongly on standard benchmarks. We validate our method through experiments
with many variant architectures and prior baselines, and demonstrate several
applications.