pwc-archive/papers-with-abstracts huggingface dataset:
Learning a Wavelet-like Auto-Encoder to Accelerate Deep Neural Networks
Authors: Tianshui Chen, Liang Lin, WangMeng Zuo, Xiaonan Luo, Lei Zhang
Date: 2017-12-20 00:00:00
Tasks: Classification, General Classification, Tensor Decomposition

Accelerating deep neural networks (DNNs) has been attracting increasing
attention as it can benefit a wide range of applications, e.g., enabling mobile
systems with limited computing resources to own powerful visual recognition
ability. A practical strategy to this goal usually relies on a two-stage
process: operating on the trained DNNs (e.g., approximating the convolutional
filters with tensor decomposition) and fine-tuning the amended network, leading
to difficulty in balancing the trade-off between acceleration and maintaining
recognition performance. In this work, aiming at a general and comprehensive
way for neural network acceleration, we develop a Wavelet-like Auto-Encoder
(WAE) that decomposes the original input image into two low-resolution channels
(sub-images) and incorporate the WAE into the classification neural networks
for joint training. The two decomposed channels, in particular, are encoded to
carry the low-frequency information (e.g., image profiles) and high-frequency
(e.g., image details or noises), respectively, and enable reconstructing the
original input image through the decoding process. Then, we feed the
low-frequency channel into a standard classification network such as VGG or
ResNet and employ a very lightweight network to fuse with the high-frequency
channel to obtain the classification result. Compared to existing DNN
acceleration solutions, our framework has the following advantages: i) it is
tolerant to any existing convolutional neural networks for classification
without amending their structures; ii) the WAE provides an interpretable way to
preserve the main components of the input image for classification.
Partial Labeled Gastric Tumor Segmentation via patch-based Reiterative Learning
Authors: Yang Nan, Gianmarc Coppola, Qiaokang Liang, Kunglin Zou, Wei Sun, Dan Zhang, Yaonan Wang, Guanzhen Yu
Date: 2017-12-20 00:00:00
Tasks: Image Segmentation, Semantic Segmentation, Tumor Segmentation

Gastric cancer is the second leading cause of cancer-related deaths
worldwide, and the major hurdle in biomedical image analysis is the
determination of the cancer extent. This assignment has high clinical relevance
and would generally require vast microscopic assessment by pathologists. Recent
advances in deep learning have produced inspiring results on biomedical image
segmentation, while its outcome is reliant on comprehensive annotation. This
requires plenty of labor costs, for the ground truth must be annotated
meticulously by pathologists. In this paper, a reiterative learning framework
was presented to train our network on partial annotated biomedical images, and
superior performance was achieved without any pre-trained or further manual
annotation. We eliminate the boundary error of patch-based model through our
overlapped region forecast algorithm. Through these advisable methods, a mean
intersection over union coefficient (IOU) of 0.883 and mean accuracy of 91.09%
on the partial labeled dataset was achieved, which made us win the 2017 China
Big Data & Artificial Intelligence Innovation and Entrepreneurship
Competitions.
Attribute CNNs for Word Spotting in Handwritten Documents
Authors: Sebastian Sudholt, Gernot Fink
Date: 2017-12-20 00:00:00
Tasks: Attribute, Segmentation, Word Spotting In Handwritten Documents

Word spotting has become a field of strong research interest in document
image analysis over the last years. Recently, AttributeSVMs were proposed which
predict a binary attribute representation. At their time, this influential
method defined the state-of-the-art in segmentation-based word spotting. In
this work, we present an approach for learning attribute representations with
Convolutional Neural Networks (CNNs). By taking a probabilistic perspective on
training CNNs, we derive two different loss functions for binary and
real-valued word string embeddings. In addition, we propose two different CNN
architectures, specifically designed for word spotting. These architectures are
able to be trained in an end-to-end fashion. In a number of experiments, we
investigate the influence of different word string embeddings and optimization
strategies. We show our Attribute CNNs to achieve state-of-the-art results for
segmentation-based word spotting on a large variety of data sets.
Accurate 3D Reconstruction of Dynamic Scenes from Monocular Image Sequences with Severe Occlusions
Authors: Vladislav Golyanik, Torben Fetzer, Didier Stricker
Date: 2017-12-20 00:00:00
Tasks: 3D Reconstruction

The paper introduces an accurate solution to dense orthographic Non-Rigid
Structure from Motion (NRSfM) in scenarios with severe occlusions or, likewise,
inaccurate correspondences. We integrate a shape prior term into variational
optimisation framework. It allows to penalize irregularities of the
time-varying structure on the per-pixel level if correspondence quality
indicator such as an occlusion tensor is available. We make a realistic
assumption that several non-occluded views of the scene are sufficient to
estimate an initial shape prior, though the entire observed scene may exhibit
non-rigid deformations. Experiments on synthetic and real image data show that
the proposed framework significantly outperforms state of the art methods for
correspondence establishment in combination with the state of the art NRSfM
methods. Together with the profound insights into optimisation methods,
implementation details for heterogeneous platforms are provided.
Recurrent Attentional Reinforcement Learning for Multi-label Image Recognition
Authors: Tianshui Chen, Zhouxia Wang, Guanbin Li, Liang Lin
Date: 2017-12-20 00:00:00
Tasks: Multi-Label Image Recognition, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recognizing multiple labels of images is a fundamental but challenging task
in computer vision, and remarkable progress has been attained by localizing
semantic-aware image regions and predicting their labels with deep
convolutional neural networks. The step of hypothesis regions (region
proposals) localization in these existing multi-label image recognition
pipelines, however, usually takes redundant computation cost, e.g., generating
hundreds of meaningless proposals with non-discriminative information and
extracting their features, and the spatial contextual dependency modeling among
the localized regions are often ignored or over-simplified. To resolve these
issues, this paper proposes a recurrent attention reinforcement learning
framework to iteratively discover a sequence of attentional and informative
regions that are related to different semantic objects and further predict
label scores conditioned on these regions. Besides, our method explicitly
models long-term dependencies among these attentional regions that help to
capture semantic label co-occurrence and thus facilitate multi-label
recognition. Extensive experiments and comparisons on two large-scale
benchmarks (i.e., PASCAL VOC and MS-COCO) show that our model achieves superior
performance over existing state-of-the-art methods in both performance and
efficiency as well as explicitly identifying image-level semantic labels to
specific object regions.
Human Action Recognition: Pose-based Attention draws focus to Hands
Authors: Fabien Baradel, Christian Wolf, Julien Mille
Date: 2017-12-20 00:00:00
Tasks: Action Recognition, Temporal Action Localization

We propose a new spatio-temporal attention based mechanism for human action
recognition able to automatically attend to the hands most involved into the
studied action and detect the most discriminative moments in an action.
Attention is handled in a recurrent manner employing Recurrent Neural Network
(RNN) and is fully-differentiable. In contrast to standard soft-attention based
mechanisms, our approach does not use the hidden RNN state as input to the
attention model. Instead, attention distributions are extracted using external
information: human articulated pose. We performed an extensive ablation study
to show the strengths of this approach and we particularly studied the
conditioning aspect of the attention mechanism. We evaluate the method on the
largest currently available human action recognition dataset, NTU-RGB+D, and
report state-of-the-art results. Other advantages of our model are certain
aspects of explanability, as the spatial and temporal attention distributions
at test time allow to study and verify on which parts of the input data the
method focuses.
Fast kNN mode seeking clustering applied to active learning
Authors: Robert P. W. Duin, Sergey Verzakov
Date: 2017-12-20 00:00:00
Tasks: Active Learning, Clustering, General Classification

A significantly faster algorithm is presented for the original kNN mode
seeking procedure. It has the advantages over the well-known mean shift
algorithm that it is feasible in high-dimensional vector spaces and results in
uniquely, well defined modes. Moreover, without any additional computational
effort it may yield a multi-scale hierarchy of clusterings. The time complexity
is just O(n^1.5). resulting computing times range from seconds for 10^4 objects
to minutes for 10^5 objects and to less than an hour for 10^6 objects. The
space complexity is just O(n). The procedure is well suited for finding large
sets of small clusters and is thereby a candidate to analyze thousands of
clusters in millions of objects.
  The kNN mode seeking procedure can be used for active learning by assigning
the clusters to the class of the modal objects of the clusters. Its feasibility
is shown by some examples with up to 1.5 million handwritten digits. The
obtained classification results based on the clusterings are compared with
those obtained by the nearest neighbor rule and the support vector classifier
based on the same labeled objects for training. It can be concluded that using
the clustering structure for classification can be significantly better than
using the trained classifiers. A drawback of using the clustering for
classification, however, is that no classifier is obtained that may be used for
out-of-sample objects.
Self-Supervised Damage-Avoiding Manipulation Strategy Optimization via Mental Simulation
Authors: Tobias Doernbach
Date: 2017-12-20 00:00:00

Everyday robotics are challenged to deal with autonomous product handling in applications like logistics or retail, possibly causing damage on the items during manipulation. Traditionally, most approaches try to minimize physical interaction with goods. However, this paper proposes to take into account any unintended object motion and to learn damage-minimizing manipulation strategies in a self-supervised way. The presented approach consists of a simulation-based planning method for an optimal manipulation sequence with respect to possible damage. The planned manipulation sequences are generalized to new, unseen scenes in the same application scenario using machine learning. This learned manipulation strategy is continuously refined in a self-supervised, simulation-in-the-loop optimization cycle during load-free times of the system, commonly known as mental simulation. In parallel, the generated manipulation strategies can be deployed in near-real time in an anytime fashion. The approach is validated on an industrial container-unloading scenario and on a retail shelf-replenishment scenario.
ADINE: An Adaptive Momentum Method for Stochastic Gradient Descent
Authors: Vishwak Srinivasan, Adepu Ravi Sankar, Vineeth N. Balasubramanian
Date: 2017-12-20 00:00:00

Two major momentum-based techniques that have achieved tremendous success in
optimization are Polyak's heavy ball method and Nesterov's accelerated
gradient. A crucial step in all momentum-based methods is the choice of the
momentum parameter $m$ which is always suggested to be set to less than $1$.
Although the choice of $m < 1$ is justified only under very strong theoretical
assumptions, it works well in practice even when the assumptions do not
necessarily hold. In this paper, we propose a new momentum based method
$\textit{ADINE}$, which relaxes the constraint of $m < 1$ and allows the
learning algorithm to use adaptive higher momentum. We motivate our hypothesis
on $m$ by experimentally verifying that a higher momentum ($\ge 1$) can help
escape saddles much faster. Using this motivation, we propose our method
$\textit{ADINE}$ that helps weigh the previous updates more (by setting the
momentum parameter $> 1$), evaluate our proposed algorithm on deep neural
networks and show that $\textit{ADINE}$ helps the learning algorithm to
converge much faster without compromising on the generalization error.
Finding Competitive Network Architectures Within a Day Using UCT
Authors: Martin Wistuba
Date: 2017-12-20 00:00:00
Tasks: GPU, Neural Architecture Search

The design of neural network architectures for a new data set is a laborious
task which requires human deep learning expertise. In order to make deep
learning available for a broader audience, automated methods for finding a
neural network architecture are vital. Recently proposed methods can already
achieve human expert level performances. However, these methods have run times
of months or even years of GPU computing time, ignoring hardware constraints as
faced by many researchers and companies. We propose the use of Monte Carlo
planning in combination with two different UCT (upper confidence bound applied
to trees) derivations to search for network architectures. We adapt the UCT
algorithm to the needs of network architecture search by proposing two ways of
sharing information between different branches of the search tree. In an
empirical study we are able to demonstrate that this method is able to find
competitive networks for MNIST, SVHN and CIFAR-10 in just a single GPU day.
Extending the search time to five GPU days, we are able to outperform human
architectures and our competitors which consider the same types of layers.
On the adoption of abductive reasoning for time series interpretation
Authors: Tomás Teijeiro, Paulo Félix
Date: 2016-09-19 00:00:00
Tasks: General Classification, Time Series, Time Series Analysis

Time series interpretation aims to provide an explanation of what is observed
in terms of its underlying processes. The present work is based on the
assumption that the common classification-based approaches to time series
interpretation suffer from a set of inherent weaknesses, whose ultimate cause
lies in the monotonic nature of the deductive reasoning paradigm. In this
document we propose a new approach to this problem, based on the initial
hypothesis that abductive reasoning properly accounts for the human ability to
identify and characterize the patterns appearing in a time series. The result
of this interpretation is a set of conjectures in the form of observations,
organized into an abstraction hierarchy and explaining what has been observed.
A knowledge-based framework and a set of algorithms for the interpretation task
are provided, implementing a hypothesize-and-test cycle guided by an
attentional mechanism. As a representative application domain, interpretation
of the electrocardiogram allows us to highlight the strengths of the proposed
approach in comparison with traditional classification-based approaches.
Light Field Segmentation From Super-pixel Graph Representation
Authors: Xianqiang Lv, Hao Zhu, Qing Wang
Date: 2017-12-20 00:00:00
Tasks: Segmentation

Efficient and accurate segmentation of light field is an important task in
computer vision and graphics. The large volume of input data and the redundancy
of light field make it an open challenge. In the paper, we propose a novel
graph representation for interactive light field segmentation based on light
field super-pixel (LFSP). The LFSP not only maintains light field redundancy,
but also greatly reduces the graph size. These advantages make LFSP useful to
improve segmentation efficiency. Based on LFSP graph structure, we present an
efficient light field segmentation algorithm using graph-cuts. Experimental
results on both synthetic and real dataset demonstrate that our method is
superior to previous light field segmentation algorithms with respect to
accuracy and efficiency.
DeepFuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairs
Authors: K. Ram Prabhakar, V. Sai Srikar, R. Venkatesh Babu
Date: 2017-12-20 00:00:00
Proceeding: ICCV 2017 10
Tasks: Deep Learning

We present a novel deep learning architecture for fusing static
multi-exposure images. Current multi-exposure fusion (MEF) approaches use
hand-crafted features to fuse input sequence. However, the weak hand-crafted
representations are not robust to varying input conditions. Moreover, they
perform poorly for extreme exposure image pairs. Thus, it is highly desirable
to have a method that is robust to varying input conditions and capable of
handling extreme exposure without artifacts. Deep representations have known to
be robust to input conditions and have shown phenomenal performance in a
supervised setting. However, the stumbling block in using deep learning for MEF
was the lack of sufficient training data and an oracle to provide the
ground-truth for supervision. To address the above issues, we have gathered a
large dataset of multi-exposure image stacks for training and to circumvent the
need for ground truth images, we propose an unsupervised deep learning
framework for MEF utilizing a no-reference quality metric as loss function. The
proposed approach uses a novel CNN architecture trained to learn the fusion
operation without reference ground truth image. The model fuses a set of common
low level features extracted from each image to generate artifact-free
perceptually pleasing results. We perform extensive quantitative and
qualitative evaluation and show that the proposed technique outperforms
existing state-of-the-art approaches for a variety of natural images.
Automatic Image Transformation for Inducing Affect
Authors: Afsheen Rafaqat Ali, Mohsen Ali
Date: 2017-07-25 00:00:00

Current image transformation and recoloring algorithms try to introduce artistic effects in the photographed images, based on user input of target image(s) or selection of pre-designed filters. These manipulations, although intended to enhance the impact of an image on the viewer, do not include the option of image transformation by specifying the affect information. In this paper we present an automatic image-transformation method that transforms the source image such that it can induce an emotional affect on the viewer, as desired by the user. Our proposed novel image emotion transfer algorithm does not require a user-specified target image. The proposed algorithm uses features extracted from top layers of deep convolutional neural network and the user-specified emotion distribution to select multiple target images from an image database for color transformation, such that the resultant image has desired emotional impact. Our method can handle more diverse set of photographs than the previous methods. We conducted a detailed user study showing the effectiveness of our proposed method. A discussion and reasoning of failure cases has also been provided, indicating inherent limitation of color-transfer based methods in the use of emotion assignment. Project Page: http://im.itu.edu.pk/affective-image-transfer/
Transformation Models in High-Dimensions
Authors: Sven Klaassen, Jannis Kueck, Martin Spindler
Date: 2017-12-20 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Transformation models are a very important tool for applied statisticians and
econometricians. In many applications, the dependent variable is transformed so
that homogeneity or normal distribution of the error holds. In this paper, we
analyze transformation models in a high-dimensional setting, where the set of
potential covariates is large. We propose an estimator for the transformation
parameter and we show that it is asymptotically normally distributed using an
orthogonalized moment condition where the nuisance functions depend on the
target parameter. In a simulation study, we show that the proposed estimator
works well in small samples. A common practice in labor economics is to
transform wage with the log-function. In this study, we test if this
transformation holds in CPS data from the United States.
Learning with Average Top-k Loss
Authors: Yanbo Fan, Siwei Lyu, Yiming Ying, Bao-Gang Hu
Date: 2017-05-24 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Binary Classification, General Classification, Learning Theory

In this work, we introduce the {\em average top-$k$} (\atk) loss as a new
aggregate loss for supervised learning, which is the average over the $k$
largest individual losses over a training dataset. We show that the \atk loss
is a natural generalization of the two widely used aggregate losses, namely the
average loss and the maximum loss, but can combine their advantages and
mitigate their drawbacks to better adapt to different data distributions.
Furthermore, it remains a convex function over all individual losses, which can
lead to convex optimization problems that can be solved effectively with
conventional gradient-based methods. We provide an intuitive interpretation of
the \atk loss based on its equivalent effect on the continuous individual loss
functions, suggesting that it can reduce the penalty on correctly classified
data. We further give a learning theory analysis of \matk learning on the
classification calibration of the \atk loss and the error bounds of \atk-SVM.
We demonstrate the applicability of minimum average top-$k$ learning for binary
classification and regression using synthetic and real datasets.
On Computationally Tractable Selection of Experiments in Measurement-Constrained Regression Models
Authors: Yining Wang, Adams Wei Yu, Aarti Singh
Date: 2016-01-09 00:00:00
Tasks: Combinatorial Optimization, regression

We derive computationally tractable methods to select a small subset of
experiment settings from a large pool of given design points. The primary focus
is on linear regression models, while the technique extends to generalized
linear models and Delta's method (estimating functions of linear regression
models) as well. The algorithms are based on a continuous relaxation of an
otherwise intractable combinatorial optimization problem, with sampling or
greedy procedures as post-processing steps. Formal approximation guarantees are
established for both algorithms, and numerical results on both synthetic and
real-world data confirm the effectiveness of the proposed methods.