pwc-archive/papers-with-abstracts huggingface dataset:
Boundary Flow: A Siamese Network that Predicts Boundary Motion without Training on Motion
Authors: Peng Lei, Fuxin Li, Sinisa Todorovic
Date: 2017-02-28 00:00:00
Proceeding: CVPR 2018 6
Tasks: Boundary Detection, Motion Estimation, Object, Optical Flow Estimation

Using deep learning, this paper addresses the problem of joint object
boundary detection and boundary motion estimation in videos, which we named
boundary flow estimation. Boundary flow is an important mid-level visual cue as
boundaries characterize objects spatial extents, and the flow indicates objects
motions and interactions. Yet, most prior work on motion estimation has focused
on dense object motion or feature points that may not necessarily reside on
boundaries. For boundary flow estimation, we specify a new fully convolutional
Siamese network (FCSN) that jointly estimates object-level boundaries in two
consecutive frames. Boundary correspondences in the two frames are predicted by
the same FCSN with a new, unconventional deconvolution approach. Finally, the
boundary flow estimate is improved with an edgelet-based filtering. Evaluation
is conducted on three tasks: boundary detection in videos, boundary flow
estimation, and optical flow estimation. On boundary detection, we achieve the
state-of-the-art performance on the benchmark VSB100 dataset. On boundary flow
estimation, we present the first results on the Sintel training dataset. For
optical flow estimation, we run the recent approach CPMFlow but on the
augmented input with our boundary-flow matches, and achieve significant
performance improvement on the Sintel benchmark.
Clustering and Retrieval Method of Immunological Memory Cell in Clonal Selection Algorithm
Authors: Takumi Ichimura, Shin Kamada
Date: 2018-04-08 00:00:00
Tasks: Clustering, General Classification, Retrieval

The clonal selection principle explains the basic features of an adaptive
immune response to a antigenic stimulus. It established the idea that only
those cells that recognize the antigens are selected to proliferate and
differentiate. This paper explains a computational implementation of the clonal
selection principle that explicitly takes into account the affinity maturation
of the immune response. Antibodies generated by the clonal selection algorithm
are clustered in some categories according to the affinity maturation, so that
immunological memory cells which respond to the specified pathogen are created.
Experimental results to classify the medical database of Coronary Heart Disease
databases are reported. For the dataset, our proposed method shows the 99.6\%
classification capability of training data.
Tourist Navigation in Android Smartphone by using Emotion Generating Calculations and Mental State Transition Networks
Authors: Takumi Ichimura, Kosuke Tanabe, Issei Tachibana
Date: 2018-04-08 00:00:00

Mental State Transition Network which consists of mental states connected to
each other is a basic concept of approximating to human psychological and
mental responses. It can represent transition from an emotional state to other
one with stimulus by calculating Emotion Generating Calculations method. A
computer agent can transit a mental state in MSTN based on analysis of emotion
by EGC method. In this paper, the Andorid EGC which the agent works in Android
smartphone can evaluate the feelings in the conversation. The tourist
navigation system with the proposed technique in this paper will be expected to
be an emotional oriented interface in Android smartphone.
Dimensionality's Blessing: Clustering Images by Underlying Distribution
Authors: Wen-Yan Lin, Siying Liu, Jian-Huang Lai, Yasuyuki Matsushita
Date: 2018-04-08 00:00:00
Proceeding: CVPR 2018 6
Tasks: Clustering

Many high dimensional vector distances tend to a constant. This is typically
considered a negative "contrast-loss" phenomenon that hinders clustering and
other machine learning techniques. We reinterpret "contrast-loss" as a
blessing. Re-deriving "contrast-loss" using the law of large numbers, we show
it results in a distribution's instances concentrating on a thin "hyper-shell".
The hollow center means apparently chaotically overlapping distributions are
actually intrinsically separable. We use this to develop
distribution-clustering, an elegant algorithm for grouping of data points by
their (unknown) underlying distribution. Distribution-clustering, creates
notably clean clusters from raw unlabeled data, estimates the number of
clusters for itself and is inherently robust to "outliers" which form their own
clusters. This enables trawling for patterns in unorganized data and may be the
key to enabling machine intelligence.
A Proposal of Interactive Growing Hierarchical SOM
Authors: Takumi Ichimura, Takashi Yamaguchi
Date: 2018-04-08 00:00:00

Self Organizing Map is trained using unsupervised learning to produce a
two-dimensional discretized representation of input space of the training
cases. Growing Hierarchical SOM is an architecture which grows both in a
hierarchical way representing the structure of data distribution and in a
horizontal way representation the size of each individual maps. The control
method of the growing degree of GHSOM by pruning off the redundant branch of
hierarchy in SOM is proposed in this paper. Moreover, the interface tool for
the proposed method called interactive GHSOM is developed. We discuss the
computation results of Iris data by using the developed tool.
Language Modeling with Generative AdversarialNetworks
Authors: Mehrad Moradshahi, Utkarsh Contractor
Date: 2018-04-08 00:00:00
Tasks: Image Generation, Language Modeling, Language Modelling, Text Generation

Generative Adversarial Networks (GANs) have been promising in the field of
image generation, however, they have been hard to train for language
generation. GANs were originally designed to output differentiable values, so
discrete language generation is challenging for them which causes high levels
of instability in training GANs. Consequently, past work has resorted to
pre-training with maximum-likelihood or training GANs without pre-training with
a WGAN objective with a gradient penalty. In this study, we present a
comparison of those approaches. Furthermore, we present the results of some
experiments that indicate better training and convergence of Wasserstein GANs
(WGANs) when a weaker regularization term is enforcing the Lipschitz
constraint.
Building Efficient CNN Architecture for Offline Handwritten Chinese Character Recognition
Authors: Zhiyuan Li, Nanjun Teng, Min Jin, Huaxiang Lu
Date: 2018-04-04 00:00:00
Tasks: Offline Handwritten Chinese Character Recognition

Deep convolutional networks based methods have brought great breakthrough in
images classification, which provides an end-to-end solution for handwritten
Chinese character recognition(HCCR) problem through learning discriminative
features automatically. Nevertheless, state-of-the-art CNNs appear to incur
huge computation cost, and require the storage of a large number of parameters
especially in fully connected layers, which is difficult to deploy such
networks into alternative hardware device with the limit of computation amount.
To solve the storage problem, we propose a novel technique called Global
Weighted Arverage Pooling for reducing the parameters in fully connected layer
without loss in accuracy. Besides, we implement a cascaded model in single CNN
by adding mid output layer to complete recognition as early as possible, which
reduces average inference time significantly. Experiments were performed on the
ICDAR-2013 offline HCCR dataset, and it is found that the proposed approach
only needs 6.9ms for classfying a chracter image on average, and achieves the
state-of-the-art accuracy of 97.1% while requiring only 3.3MB for storage.
Recurrent Saliency Transformation Network: Incorporating Multi-Stage Visual Cues for Small Organ Segmentation
Authors: Qihang Yu, Lingxi Xie, Yan Wang, Yuyin Zhou, Elliot K. Fishman, Alan L. Yuille
Date: 2017-09-13 00:00:00
Proceeding: CVPR 2018 6
Tasks: Organ Segmentation, Pancreas Segmentation, Segmentation

We aim at segmenting small organs (e.g., the pancreas) from abdominal CT
scans. As the target often occupies a relatively small region in the input
image, deep neural networks can be easily confused by the complex and variable
background. To alleviate this, researchers proposed a coarse-to-fine approach,
which used prediction from the first (coarse) stage to indicate a smaller input
region for the second (fine) stage. Despite its effectiveness, this algorithm
dealt with two stages individually, which lacked optimizing a global energy
function, and limited its ability to incorporate multi-stage visual cues.
Missing contextual information led to unsatisfying convergence in iterations,
and that the fine stage sometimes produced even lower segmentation accuracy
than the coarse stage.
  This paper presents a Recurrent Saliency Transformation Network. The key
innovation is a saliency transformation module, which repeatedly converts the
segmentation probability map from the previous iteration as spatial weights and
applies these weights to the current iteration. This brings us two-fold
benefits. In training, it allows joint optimization over the deep networks
dealing with different input scales. In testing, it propagates multi-stage
visual information throughout iterations to improve segmentation accuracy.
Experiments in the NIH pancreas segmentation dataset demonstrate the
state-of-the-art accuracy, which outperforms the previous best by an average of
over 2%. Much higher accuracies are also reported on several small organs in a
larger dataset collected by ourselves. In addition, our approach enjoys better
convergence properties, making it more efficient and reliable in practice.
Single-Shot Object Detection with Enriched Semantics
Authors: Zhishuai Zhang, Siyuan Qiao, Cihang Xie, Wei Shen, Bo wang, Alan L. Yuille
Date: 2017-12-01 00:00:00
Proceeding: CVPR 2018 6
Tasks: GPU, Object, object-detection, Object Detection, Segmentation, Semantic Segmentation

We propose a novel single shot object detection network named Detection with
Enriched Semantics (DES). Our motivation is to enrich the semantics of object
detection features within a typical deep detector, by a semantic segmentation
branch and a global activation module. The segmentation branch is supervised by
weak segmentation ground-truth, i.e., no extra annotation is required. In
conjunction with that, we employ a global activation module which learns
relationship between channels and object classes in a self-supervised manner.
Comprehensive experimental results on both PASCAL VOC and MS COCO detection
datasets demonstrate the effectiveness of the proposed method. In particular,
with a VGG16 based DES, we achieve an mAP of 81.7 on VOC2007 test and an mAP of
32.8 on COCO test-dev with an inference speed of 31.5 milliseconds per image on
a Titan Xp GPU. With a lower resolution version, we achieve an mAP of 79.7 on
VOC2007 with an inference speed of 13.0 milliseconds per image.
Kernel Conditional Exponential Family
Authors: Michael Arbel, Arthur Gretton
Date: 2017-11-15 00:00:00

A nonparametric family of conditional distributions is introduced, which
generalizes conditional exponential families using functional parameters in a
suitable RKHS. An algorithm is provided for learning the generalized natural
parameter, and consistency of the estimator is established in the well
specified case. In experiments, the new method generally outperforms a
competing approach with consistency guarantees, and is competitive with a deep
conditional density model on datasets that exhibit abrupt transitions and
heteroscedasticity.
Moving Beyond Sub-Gaussianity in High-Dimensional Statistics: Applications in Covariance Estimation and Linear Regression
Authors: Arun Kumar Kuchibhotla, Abhishek Chakrabortty
Date: 2018-04-08 00:00:00
Tasks: regression

Concentration inequalities form an essential toolkit in the study of high dimensional (HD) statistical methods. Most of the relevant statistics literature in this regard is based on sub-Gaussian or sub-exponential tail assumptions. In this paper, we first bring together various probabilistic inequalities for sums of independent random variables under much more general exponential type (namely sub-Weibull) tail assumptions. These results extract a part sub-Gaussian tail behavior in finite samples, matching the asymptotics governed by the central limit theorem, and are compactly represented in terms of a new Orlicz quasi-norm - the Generalized Bernstein-Orlicz norm - that typifies such tail behaviors. We illustrate the usefulness of these inequalities through the analysis of four fundamental problems in HD statistics. In the first two problems, we study the rate of convergence of the sample covariance matrix in terms of the maximum elementwise norm and the maximum k-sub-matrix operator norm which are key quantities of interest in bootstrap, HD covariance matrix estimation and HD inference. The third example concerns the restricted eigenvalue condition, required in HD linear regression, which we verify for all sub-Weibull random vectors through a unified analysis, and also prove a more general result related to restricted strong convexity in the process. In the final example, we consider the Lasso estimator for linear regression and establish its rate of convergence under much weaker than usual tail assumptions (on the errors as well as the covariates), while also allowing for misspecified models and both fixed and random design. To our knowledge, these are the first such results for Lasso obtained in this generality. The common feature in all our results over all the examples is that the convergence rates under most exponential tails match the usual ones under sub-Gaussian assumptions.
Real-world Noisy Image Denoising: A New Benchmark
Authors: Jun Xu, Hui Li, Zhetong Liang, David Zhang, Lei Zhang
Date: 2018-04-07 00:00:00
Tasks: Denoising, Image Denoising

Most of previous image denoising methods focus on additive white Gaussian
noise (AWGN). However,the real-world noisy image denoising problem with the
advancing of the computer vision techiniques. In order to promote the study on
this problem while implementing the concurrent real-world image denoising
datasets, we construct a new benchmark dataset which contains comprehensive
real-world noisy images of different natural scenes. These images are captured
by different cameras under different camera settings. We evaluate the different
denoising methods on our new dataset as well as previous datasets. Extensive
experimental results demonstrate that the recently proposed methods designed
specifically for realistic noise removal based on sparse or low rank theories
achieve better denoising performance and are more robust than other competing
methods, and the newly proposed dataset is more challenging. The constructed
dataset of real photographs is publicly available at
\url{https://github.com/csjunxu/PolyUDataset} for researchers to investigate
new real-world image denoising methods. We will add more analysis on the noise
statistics in the real photographs of our new dataset in the next version of
this article.
Supervising Feature Influence
Authors: Shayak Sen, Piotr Mardziel, Anupam Datta, Matthew Fredrikson
Date: 2018-03-28 00:00:00
Tasks: Active Learning

Causal influence measures for machine learnt classifiers shed light on the
reasons behind classification, and aid in identifying influential input
features and revealing their biases. However, such analyses involve evaluating
the classifier using datapoints that may be atypical of its training
distribution. Standard methods for training classifiers that minimize empirical
risk do not constrain the behavior of the classifier on such datapoints. As a
result, training to minimize empirical risk does not distinguish among
classifiers that agree on predictions in the training distribution but have
wildly different causal influences. We term this problem covariate shift in
causal testing and formally characterize conditions under which it arises. As a
solution to this problem, we propose a novel active learning algorithm that
constrains the influence measures of the trained model. We prove that any two
predictors whose errors are close on both the original training distribution
and the distribution of atypical points are guaranteed to have causal
influences that are also close. Further, we empirically demonstrate with
synthetic labelers that our algorithm trains models that (i) have similar
causal influences as the labeler's model, and (ii) generalize better to
out-of-distribution points while (iii) retaining their accuracy on
in-distribution points.
Simple Models for Word Formation in English Slang
Authors: Vivek Kulkarni, William Yang Wang
Date: 2018-04-07 00:00:00

We propose generative models for three types of extra-grammatical word
formation phenomena abounding in English slang: Blends, Clippings, and
Reduplicatives. Adopting a data-driven approach coupled with linguistic
knowledge, we propose simple models with state of the art performance on human
annotated gold standard datasets. Overall, our models reveal insights into the
generative processes of word formation in slang -- insights which are
increasingly relevant in the context of the rising prevalence of slang and
non-standard varieties on the Internet.
Training Multi-organ Segmentation Networks with Sample Selection by Relaxed Upper Confident Bound
Authors: Yan Wang, Yuyin Zhou, Peng Tang, Wei Shen, Elliot K. Fishman, Alan L. Yuille
Date: 2018-04-07 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Organ Segmentation, Segmentation, Semantic Segmentation

Deep convolutional neural networks (CNNs), especially fully convolutional
networks, have been widely applied to automatic medical image segmentation
problems, e.g., multi-organ segmentation. Existing CNN-based segmentation
methods mainly focus on looking for increasingly powerful network
architectures, but pay less attention to data sampling strategies for training
networks more effectively. In this paper, we present a simple but effective
sample selection method for training multi-organ segmentation networks. Sample
selection exhibits an exploitation-exploration strategy, i.e., exploiting hard
samples and exploring less frequently visited samples. Based on the fact that
very hard samples might have annotation errors, we propose a new sample
selection policy, named Relaxed Upper Confident Bound (RUCB). Compared with
other sample selection policies, e.g., Upper Confident Bound (UCB), it exploits
a range of hard samples rather than being stuck with a small set of very hard
ones, which mitigates the influence of annotation errors during training. We
apply this new sample selection policy to training a multi-organ segmentation
network on a dataset containing 120 abdominal CT scans and show that it boosts
segmentation performance significantly.
Estimation of Camera Locations in Highly Corrupted Scenarios: All About that Base, No Shape Trouble
Authors: Yunpeng Shi, Gilad Lerman
Date: 2018-04-07 00:00:00
Proceeding: CVPR 2018 6
Tasks: All

We propose a strategy for improving camera location estimation in structure
from motion. Our setting assumes highly corrupted pairwise directions (i.e.,
normalized relative location vectors), so there is a clear room for improving
current state-of-the-art solutions for this problem. Our strategy identifies
severely corrupted pairwise directions by using a geometric consistency
condition. It then selects a cleaner set of pairwise directions as a
preprocessing step for common solvers. We theoretically guarantee the
successful performance of a basic version of our strategy under a synthetic
corruption model. Numerical results on artificial and real data demonstrate the
significant improvement obtained by our strategy.
Delayed Impact of Fair Machine Learning
Authors: Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, Moritz Hardt
Date: 2018-03-12 00:00:00
Proceeding: ICML 2018 7
Tasks: BIG-bench Machine Learning, Fairness

Fairness in machine learning has predominantly been studied in static
classification settings without concern for how decisions change the underlying
population over time. Conventional wisdom suggests that fairness criteria
promote the long-term well-being of those groups they aim to protect.
  We study how static fairness criteria interact with temporal indicators of
well-being, such as long-term improvement, stagnation, and decline in a
variable of interest. We demonstrate that even in a one-step feedback model,
common fairness criteria in general do not promote improvement over time, and
may in fact cause harm in cases where an unconstrained objective would not.
  We completely characterize the delayed impact of three standard criteria,
contrasting the regimes in which these exhibit qualitatively different
behavior. In addition, we find that a natural form of measurement error
broadens the regime in which fairness criteria perform favorably.
  Our results highlight the importance of measurement and temporal modeling in
the evaluation of fairness criteria, suggesting a range of new challenges and
trade-offs.
Multi-Temporal Land Cover Classification with Sequential Recurrent Encoders
Authors: Marc Rußwurm, Marco Körner
Date: 2018-02-06 00:00:00
Proceeding: International Journal of Geo-Information 2018 3
Tasks: Classification, Decoder, Earth Observation, General Classification, Land Cover Classification, Machine Translation, speech-recognition, Speech Recognition, UNET Segmentation

Earth observation (EO) sensors deliver data with daily or weekly temporal
resolution. Most land use and land cover (LULC) approaches, however, expect
cloud-free and mono-temporal observations. The increasing temporal capabilities
of today's sensors enables the use of temporal, along with spectral and spatial
features. Domains, such as speech recognition or neural machine translation,
work with inherently temporal data and, today, achieve impressive results using
sequential encoder-decoder structures. Inspired by these sequence-to-sequence
models, we adapt an encoder structure with convolutional recurrent layers in
order to approximate a phenological model for vegetation classes based on a
temporal sequence of Sentinel 2 (S2) images. In our experiments, we visualize
internal activations over a sequence of cloudy and non-cloudy images and find
several recurrent cells, which reduce the input activity for cloudy
observations. Hence, we assume that our network has learned cloud-filtering
schemes solely from input data, which could alleviate the need for tedious
cloud-filtering as a preprocessing step for many EO approaches. Moreover, using
unfiltered temporal series of top-of-atmosphere (TOA) reflectance data, we
achieved in our experiments state-of-the-art classification accuracies on a
large number of crop classes with minimal preprocessing compared to other
classification approaches.