pwc-archive/papers-with-abstracts huggingface dataset:
Learning Unified Embedding for Apparel Recognition
Authors: Yang Song, Yuan Li, Bo Wu, Chao-Yeh Chen, Xiao Zhang, Hartwig Adam
Date: 2017-07-19 00:00:00
Tasks: Retrieval, Triplet

In apparel recognition, specialized models (e.g. models trained for a
particular vertical like dresses) can significantly outperform general models
(i.e. models that cover a wide range of verticals). Therefore, deep neural
network models are often trained separately for different verticals. However,
using specialized models for different verticals is not scalable and expensive
to deploy. This paper addresses the problem of learning one unified embedding
model for multiple object verticals (e.g. all apparel classes) without
sacrificing accuracy. The problem is tackled from two aspects: training data
and training difficulty. On the training data aspect, we figure out that for a
single model trained with triplet loss, there is an accuracy sweet spot in
terms of how many verticals are trained together. To ease the training
difficulty, a novel learning scheme is proposed by using the output from
specialized models as learning targets so that L2 loss can be used instead of
triplet loss. This new loss makes the training easier and make it possible for
more efficient use of the feature space. The end result is a unified model
which can achieve the same retrieval accuracy as a number of separate
specialized models, while having the model complexity as one. The effectiveness
of our approach is shown in experiments.
Learning with Rethinking: Recurrently Improving Convolutional Neural Networks through Feedback
Authors: Xin Li, Zequn Jie, Jiashi Feng, Changsong Liu, Shuicheng Yan
Date: 2017-08-15 00:00:00

Recent years have witnessed the great success of convolutional neural network
(CNN) based models in the field of computer vision. CNN is able to learn
hierarchically abstracted features from images in an end-to-end training
manner. However, most of the existing CNN models only learn features through a
feedforward structure and no feedback information from top to bottom layers is
exploited to enable the networks to refine themselves. In this paper, we
propose a "Learning with Rethinking" algorithm. By adding a feedback layer and
producing the emphasis vector, the model is able to recurrently boost the
performance based on previous prediction. Particularly, it can be employed to
boost any pre-trained models. This algorithm is tested on four object
classification benchmark datasets: CIFAR-100, CIFAR-10, MNIST-background-image
and ILSVRC-2012 dataset. These results have demonstrated the advantage of
training CNN models with the proposed feedback mechanism.
Learning Light Transport the Reinforced Way
Authors: Ken Dahm, Alexander Keller
Date: 2017-01-25 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We show that the equations of reinforcement learning and light transport
simulation are related integral equations. Based on this correspondence, a
scheme to learn importance while sampling path space is derived. The new
approach is demonstrated in a consistent light transport simulation algorithm
that uses reinforcement learning to progressively learn where light comes from.
As using this information for importance sampling includes information about
visibility, too, the number of light transport paths with zero contribution is
dramatically reduced, resulting in much less noisy images within a fixed time
budget.
Comparison of Decoding Strategies for CTC Acoustic Models
Authors: Thomas Zenkel, Ramon Sanabria, Florian Metze, Jan Niehues, Matthias Sperber, Sebastian Stüker, Alex Waibel
Date: 2017-08-15 00:00:00
Tasks: Language Modeling, Language Modelling, speech-recognition, Speech Recognition

Connectionist Temporal Classification has recently attracted a lot of
interest as it offers an elegant approach to building acoustic models (AMs) for
speech recognition. The CTC loss function maps an input sequence of observable
feature vectors to an output sequence of symbols. Output symbols are
conditionally independent of each other under CTC loss, so a language model
(LM) can be incorporated conveniently during decoding, retaining the
traditional separation of acoustic and linguistic components in ASR. For fixed
vocabularies, Weighted Finite State Transducers provide a strong baseline for
efficient integration of CTC AMs with n-gram LMs. Character-based neural LMs
provide a straight forward solution for open vocabulary speech recognition and
all-neural models, and can be decoded with beam search. Finally,
sequence-to-sequence models can be used to translate a sequence of individual
sounds into a word string. We compare the performance of these three
approaches, and analyze their error patterns, which provides insightful
guidance for future research and development in this important area.
Actively Learning what makes a Discrete Sequence Valid
Authors: David Janz, Jos van der Westhuizen, José Miguel Hernández-Lobato
Date: 2017-08-15 00:00:00
Tasks: Active Learning, valid

Deep learning techniques have been hugely successful for traditional
supervised and unsupervised machine learning problems. In large part, these
techniques solve continuous optimization problems. Recently however, discrete
generative deep learning models have been successfully used to efficiently
search high-dimensional discrete spaces. These methods work by representing
discrete objects as sequences, for which powerful sequence-based deep models
can be employed. Unfortunately, these techniques are significantly hindered by
the fact that these generative models often produce invalid sequences. As a
step towards solving this problem, we propose to learn a deep recurrent
validator model. Given a partial sequence, our model learns the probability of
that sequence occurring as the beginning of a full valid sequence. Thus this
identifies valid versus invalid sequences and crucially it also provides
insight about how individual sequence elements influence the validity of
discrete objects. To learn this model we propose an approach inspired by
seminal work in Bayesian active learning. On a synthetic dataset, we
demonstrate the ability of our model to distinguish valid and invalid
sequences. We believe this is a key step toward learning generative models that
faithfully produce valid discrete objects.
Self Adversarial Training for Human Pose Estimation
Authors: Chia-Jung Chou, Jui-Ting Chien, Hwann-Tzong Chen
Date: 2017-07-08 00:00:00
Tasks: Pose Estimation

This paper presents a deep learning based approach to the problem of human
pose estimation. We employ generative adversarial networks as our learning
paradigm in which we set up two stacked hourglass networks with the same
architecture, one as the generator and the other as the discriminator. The
generator is used as a human pose estimator after the training is done. The
discriminator distinguishes ground-truth heatmaps from generated ones, and
back-propagates the adversarial loss to the generator. This process enables the
generator to learn plausible human body configurations and is shown to be
useful for improving the prediction accuracy.
Octopus: A Framework for Cost-Quality-Time Optimization in Crowdsourcing
Authors: Karan Goel, Shreya Rajpal, Mausam
Date: 2017-02-12 00:00:00
Tasks: AI Agent

We present Octopus, an AI agent to jointly balance three conflicting task
objectives on a micro-crowdsourcing marketplace - the quality of work, total
cost incurred, and time to completion. Previous control agents have mostly
focused on cost-quality, or cost-time tradeoffs, but not on directly
controlling all three in concert. A naive formulation of three-objective
optimization is intractable; Octopus takes a hierarchical POMDP approach, with
three different components responsible for setting the pay per task, selecting
the next task, and controlling task-level quality. We demonstrate that Octopus
significantly outperforms existing state-of-the-art approaches on real
experiments. We also deploy Octopus on Amazon Mechanical Turk, showing its
ability to manage tasks in a real-world dynamic setting.
Extractive Summarization using Deep Learning
Authors: Sukriti Verma, Vagisha Nidhi
Date: 2017-08-15 00:00:00
Tasks: Articles, Deep Learning, Extractive Summarization, Text Summarization

This paper proposes a text summarization approach for factual reports using a
deep learning model. This approach consists of three phases: feature
extraction, feature enhancement, and summary generation, which work together to
assimilate core information and generate a coherent, understandable summary. We
are exploring various features to improve the set of sentences selected for the
summary, and are using a Restricted Boltzmann Machine to enhance and abstract
those features to improve resultant accuracy without losing any important
information. The sentences are scored based on those enhanced features and an
extractive summary is constructed. Experimentation carried out on several
articles demonstrates the effectiveness of the proposed approach. Source code
available at: https://github.com/vagisha-nidhi/TextSummarizer
S-OHEM: Stratified Online Hard Example Mining for Object Detection
Authors: Minne Li, Zhaoning Zhang, Hao Yu, Xinyuan Chen, Dongsheng Li
Date: 2017-05-05 00:00:00
Tasks: object-detection, Object Detection

One of the major challenges in object detection is to propose detectors with
highly accurate localization of objects. The online sampling of high-loss
region proposals (hard examples) uses the multitask loss with equal weight
settings across all loss types (e.g, classification and localization, rigid and
non-rigid categories) and ignores the influence of different loss distributions
throughout the training process, which we find essential to the training
efficacy. In this paper, we present the Stratified Online Hard Example Mining
(S-OHEM) algorithm for training higher efficiency and accuracy detectors.
S-OHEM exploits OHEM with stratified sampling, a widely-adopted sampling
technique, to choose the training examples according to this influence during
hard example mining, and thus enhance the performance of object detectors. We
show through systematic experiments that S-OHEM yields an average precision
(AP) improvement of 0.5% on rigid categories of PASCAL VOC 2007 for both the
IoU threshold of 0.6 and 0.7. For KITTI 2012, both results of the same metric
are 1.6%. Regarding the mean average precision (mAP), a relative increase of
0.3% and 0.5% (1% and 0.5%) is observed for VOC07 (KITTI12) using the same set
of IoU threshold. Also, S-OHEM is easy to integrate with existing region-based
detectors and is capable of acting with post-recognition level regressors.
Knock-Knock: Acoustic Object Recognition by using Stacked Denoising Autoencoders
Authors: Shan Luo, Leqi Zhu, Kaspar Althoefer, Hongbin Liu
Date: 2017-08-15 00:00:00
Tasks: Deep Learning, Denoising, Object, Object Recognition

This paper presents a successful application of deep learning for object
recognition based on acoustic data. The shortcomings of previously employed
approaches where handcrafted features describing the acoustic data are being
used, include limiting the capability of the found representation to be widely
applicable and facing the risk of capturing only insignificant characteristics
for a task. In contrast, there is no need to define the feature representation
format when using multilayer/deep learning architecture methods: features can
be learned from raw sensor data without defining discriminative characteristics
a-priori. In this paper, stacked denoising autoencoders are applied to train a
deep learning model. Knocking each object in our test set 120 times with a
marker pen to obtain the auditory data, thirty different objects were
successfully classified in our experiment and each object was knocked 120 times
by a marker pen to obtain the auditory data. By employing the proposed deep
learning framework, a high accuracy of 91.50% was achieved. A traditional
method using handcrafted features with a shallow classifier was taken as a
benchmark and the attained recognition rate was only 58.22%. Interestingly, a
recognition rate of 82.00% was achieved when using a shallow classifier with
raw acoustic data as input. In addition, we could show that the time taken to
classify one object using deep learning was far less (by a factor of more than
6) than utilizing the traditional method. It was also explored how different
model parameters in our deep architecture affect the recognition performance.
Theoretical Foundation of Co-Training and Disagreement-Based Algorithms
Authors: Wei Wang, Zhi-Hua Zhou
Date: 2017-08-15 00:00:00

Disagreement-based approaches generate multiple classifiers and exploit the
disagreement among them with unlabeled data to improve learning performance.
Co-training is a representative paradigm of them, which trains two classifiers
separately on two sufficient and redundant views; while for the applications
where there is only one view, several successful variants of co-training with
two different classifiers on single-view data instead of two views have been
proposed. For these disagreement-based approaches, there are several important
issues which still are unsolved, in this article we present theoretical
analyses to address these issues, which provides a theoretical foundation of
co-training and disagreement-based approaches.
SafetyNet: Detecting and Rejecting Adversarial Examples Robustly
Authors: Jiajun Lu, Theerasit Issaranon, David Forsyth
Date: 2017-04-01 00:00:00
Proceeding: ICCV 2017 10

We describe a method to produce a network where current methods such as
DeepFool have great difficulty producing adversarial samples. Our construction
suggests some insights into how deep networks work. We provide a reasonable
analyses that our construction is difficult to defeat, and show experimentally
that our method is hard to defeat with both Type I and Type II attacks using
several standard networks and datasets. This SafetyNet architecture is used to
an important and novel application SceneProof, which can reliably detect
whether an image is a picture of a real scene or not. SceneProof applies to
images captured with depth maps (RGBD images) and checks if a pair of image and
depth map is consistent. It relies on the relative difficulty of producing
naturalistic depth maps for images in post processing. We demonstrate that our
SafetyNet is robust to adversarial examples built from currently known
attacking approaches.
Bringing Background into the Foreground: Making All Classes Equal in Weakly-supervised Video Semantic Segmentation
Authors: Fatemeh Sadat Saleh, Mohammad Sadegh Aliakbarian, Mathieu Salzmann, Lars Petersson, Jose M. Alvarez
Date: 2017-08-15 00:00:00
Proceeding: ICCV 2017 10
Tasks: All, Autonomous Navigation, Segmentation, Semantic Segmentation, Video Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

Pixel-level annotations are expensive and time-consuming to obtain. Hence,
weak supervision using only image tags could have a significant impact in
semantic segmentation. Recent years have seen great progress in
weakly-supervised semantic segmentation, whether from a single image or from
videos. However, most existing methods are designed to handle a single
background class. In practical applications, such as autonomous navigation, it
is often crucial to reason about multiple background classes. In this paper, we
introduce an approach to doing so by making use of classifier heatmaps. We then
develop a two-stream deep architecture that jointly leverages appearance and
motion, and design a loss based on our heatmaps to train it. Our experiments
demonstrate the benefits of our classifier heatmaps and of our two-stream
architecture on challenging urban scene datasets and on the YouTube-Objects
benchmark, where we obtain state-of-the-art results.
Sparse Partially Collapsed MCMC for Parallel Inference in Topic Models
Authors: Måns Magnusson, Leif Jonsson, Mattias Villani, David Broman
Date: 2015-06-11 00:00:00
Tasks: Topic Models

Topic models, and more specifically the class of Latent Dirichlet Allocation
(LDA), are widely used for probabilistic modeling of text. MCMC sampling from
the posterior distribution is typically performed using a collapsed Gibbs
sampler. We propose a parallel sparse partially collapsed Gibbs sampler and
compare its speed and efficiency to state-of-the-art samplers for topic models
on five well-known text corpora of differing sizes and properties. In
particular, we propose and compare two different strategies for sampling the
parameter block with latent topic indicators. The experiments show that the
increase in statistical inefficiency from only partial collapsing is smaller
than commonly assumed, and can be more than compensated by the speedup from
parallelization and sparsity on larger corpora. We also prove that the
partially collapsed samplers scale well with the size of the corpus. The
proposed algorithm is fast, efficient, exact, and can be used in more modeling
situations than the ordinary collapsed sampler.
Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks
Authors: Jun Xiao, Hao Ye, Xiangnan He, Hanwang Zhang, Fei Wu, Tat-Seng Chua
Date: 2017-08-15 00:00:00
Tasks: regression

Factorization Machines (FMs) are a supervised learning approach that enhances
the linear regression model by incorporating the second-order feature
interactions. Despite effectiveness, FM can be hindered by its modelling of all
feature interactions with the same weight, as not all feature interactions are
equally useful and predictive. For example, the interactions with useless
features may even introduce noises and adversely degrade the performance. In
this work, we improve FM by discriminating the importance of different feature
interactions. We propose a novel model named Attentional Factorization Machine
(AFM), which learns the importance of each feature interaction from data via a
neural attention network. Extensive experiments on two real-world datasets
demonstrate the effectiveness of AFM. Empirically, it is shown on regression
task AFM betters FM with a $8.6\%$ relative improvement, and consistently
outperforms the state-of-the-art deep learning methods Wide&Deep and DeepCross
with a much simpler structure and fewer model parameters. Our implementation of
AFM is publicly available at:
https://github.com/hexiangnan/attentional_factorization_machine
Learning body-affordances to simplify action spaces
Authors: Nicholas Guttenberg, Martin Biehl, Ryota Kanai
Date: 2017-08-15 00:00:00

Controlling embodied agents with many actuated degrees of freedom is a
challenging task. We propose a method that can discover and interpolate between
context dependent high-level actions or body-affordances. These provide an
abstract, low-dimensional interface indexing high-dimensional and time-
extended action policies. Our method is related to recent ap- proaches in the
machine learning literature but is conceptually simpler and easier to
implement. More specifically our method requires the choice of a n-dimensional
target sensor space that is endowed with a distance metric. The method then
learns an also n-dimensional embedding of possibly reactive body-affordances
that spread as far as possible throughout the target sensor space.
Fluency-Guided Cross-Lingual Image Captioning
Authors: Weiyu Lan, Xirong Li, Jianfeng Dong
Date: 2017-08-15 00:00:00
Tasks: Image Captioning

Image captioning has so far been explored mostly in English, as most
available datasets are in this language. However, the application of image
captioning should not be restricted by language. Only few studies have been
conducted for image captioning in a cross-lingual setting. Different from these
works that manually build a dataset for a target language, we aim to learn a
cross-lingual captioning model fully from machine-translated sentences. To
conquer the lack of fluency in the translated sentences, we propose in this
paper a fluency-guided learning framework. The framework comprises a module to
automatically estimate the fluency of the sentences and another module to
utilize the estimated fluency scores to effectively train an image captioning
model for the target language. As experiments on two bilingual
(English-Chinese) datasets show, our approach improves both fluency and
relevance of the generated captions in Chinese, but without using any manually
written sentences from the target language.
Discriminatively Trained Latent Ordinal Model for Video Classification
Authors: Karan Sikka, Gaurav Sharma
Date: 2016-08-08 00:00:00
Tasks: Action Recognition, Classification, General Classification, model, Multiple Instance Learning, Temporal Action Localization, Video Classification, Weakly-supervised Learning

We study the problem of video classification for facial analysis and human
action recognition. We propose a novel weakly supervised learning method that
models the video as a sequence of automatically mined, discriminative
sub-events (eg. onset and offset phase for "smile", running and jumping for
"highjump"). The proposed model is inspired by the recent works on Multiple
Instance Learning and latent SVM/HCRF -- it extends such frameworks to model
the ordinal aspect in the videos, approximately. We obtain consistent
improvements over relevant competitive baselines on four challenging and
publicly available video based facial analysis datasets for prediction of
expression, clinical pain and intent in dyadic conversations and on three
challenging human action datasets. We also validate the method with qualitative
results and show that they largely support the intuitions behind the method.
Optimization of Ensemble Supervised Learning Algorithms for Increased Sensitivity, Specificity, and AUC of Population-Based Colorectal Cancer Screenings
Authors: Anirudh Kamath, Aditya Singh, Raj Ramnani, Ayush Vyas, Jay Shenoy
Date: 2017-08-13 00:00:00
Tasks: Sensitivity, Specificity

Over 150,000 new people in the United States are diagnosed with colorectal
cancer each year. Nearly a third die from it (American Cancer Society). The
only approved noninvasive diagnosis tools currently involve fecal blood count
tests (FOBTs) or stool DNA tests. Fecal blood count tests take only five
minutes and are available over the counter for as low as \$15. They are highly
specific, yet not nearly as sensitive, yielding a high percentage (25%) of
false negatives (Colon Cancer Alliance). Moreover, FOBT results are far too
generalized, meaning that a positive result could mean much more than just
colorectal cancer, and could just as easily mean hemorrhoids, anal fissure,
proctitis, Crohn's disease, diverticulosis, ulcerative colitis, rectal ulcer,
rectal prolapse, ischemic colitis, angiodysplasia, rectal trauma, proctitis
from radiation therapy, and others. Stool DNA tests, the modern benchmark for
CRC screening, have a much higher sensitivity and specificity, but also cost
\$600, take two weeks to process, and are not for high-risk individuals or
people with a history of polyps. To yield a cheap and effective CRC screening
alternative, a unique ensemble-based classification algorithm is put in place
that considers the FIT result, BMI, smoking history, and diabetic status of
patients. This method is tested under ten-fold cross validation to have a .95
AUC, 92% specificity, 89% sensitivity, .88 F1, and 90% precision. Once
clinically validated, this test promises to be cheaper, faster, and potentially
more accurate when compared to a stool DNA test.