pwc-archive/papers-with-abstracts huggingface dataset:
Synaptic Strength For Convolutional Neural Network
Authors: Chen Lin, Zhao Zhong, Wei Wu, Junjie Yan
Date: 2018-11-06 00:00:00
Proceeding: NeurIPS 2018 12

Convolutional Neural Networks(CNNs) are both computation and memory intensive
which hindered their deployment in mobile devices. Inspired by the relevant
concept in neural science literature, we propose Synaptic Pruning: a
data-driven method to prune connections between input and output feature maps
with a newly proposed class of parameters called Synaptic Strength. Synaptic
Strength is designed to capture the importance of a connection based on the
amount of information it transports. Experiment results show the effectiveness
of our approach. On CIFAR-10, we prune connections for various CNN models with
up to 96% , which results in significant size reduction and computation saving.
Further evaluation on ImageNet demonstrates that synaptic pruning is able to
discover efficient models which is competitive to state-of-the-art compact CNNs
such as MobileNet-V2 and NasNet-Mobile. Our contribution is summarized as
following: (1) We introduce Synaptic Strength, a new class of parameters for
CNNs to indicate the importance of each connections. (2) Our approach can prune
various CNNs with high compression without compromising accuracy. (3) Further
investigation shows, the proposed Synaptic Strength is a better indicator for
kernel pruning compared with the previous approach in both empirical result and
theoretical analysis.
WordNet-feelings: A linguistic categorisation of human feelings
Authors: Advaith Siddharthan, Nicolas Cherbuin, Paul J. Eslinger, Kasia Kozlowska, Nora A. Murphy, Leroy Lowe
Date: 2018-11-06 00:00:00

In this article, we present the first in depth linguistic study of human
feelings. While there has been substantial research on incorporating some
affective categories into linguistic analysis (e.g. sentiment, and to a lesser
extent, emotion), the more diverse category of human feelings has thus far not
been investigated. We surveyed the extensive interdisciplinary literature
around feelings to construct a working definition of what constitutes a feeling
and propose 9 broad categories of feeling. We identified potential feeling
words based on their pointwise mutual information with morphological variants
of the word `feel' in the Google n-gram corpus, and present a manual annotation
exercise where 317 WordNet senses of one hundred of these words were
categorised as `not a feeling' or as one of the 9 proposed categories of
feeling. We then proceeded to annotate 11386 WordNet senses of all these words
to create WordNet-feelings, a new affective dataset that identifies 3664 word
senses as feelings, and associates each of these with one of the 9 categories
of feeling. WordNet-feelings can be used in conjunction with other datasets
such as SentiWordNet that annotate word senses with complementary affective
properties such as valence and intensity.
A Bit Too Much? High Speed Imaging from Sparse Photon Counts
Authors: Paramanand Chandramouli, Samuel Burri, Claudio Bruschini, Edoardo Charbon, Andreas Kolb
Date: 2018-11-06 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Recent advances in photographic sensing technologies have made it possible to achieve light detection in terms of a single photon. Photon counting sensors are being increasingly used in many diverse applications. We address the problem of jointly recovering spatial and temporal scene radiance from very few photon counts. Our ConvNet-based scheme effectively combines spatial and temporal information present in measurements to reduce noise. We demonstrate that using our method one can acquire videos at a high frame rate and still achieve good quality signal-to-noise ratio. Experiments show that the proposed scheme performs quite well in different challenging scenarios while the existing approaches are unable to handle them.
DeepChannel: Salience Estimation by Contrastive Learning for Extractive Document Summarization
Authors: Jiaxin Shi, Chen Liang, Lei Hou, Juanzi Li, Zhiyuan Liu, Hanwang Zhang
Date: 2018-11-06 00:00:00
Tasks: Contrastive Learning, Document Summarization, Extractive Document Summarization, Extractive Text Summarization

We propose DeepChannel, a robust, data-efficient, and interpretable neural
model for extractive document summarization. Given any document-summary pair,
we estimate a salience score, which is modeled using an attention-based deep
neural network, to represent the salience degree of the summary for yielding
the document. We devise a contrastive training strategy to learn the salience
estimation network, and then use the learned salience score as a guide and
iteratively extract the most salient sentences from the document as our
generated summary. In experiments, our model not only achieves state-of-the-art
ROUGE scores on CNN/Daily Mail dataset, but also shows strong robustness in the
out-of-domain test on DUC2007 test set. Moreover, our model reaches a ROUGE-1
F-1 score of 39.41 on CNN/Daily Mail test set with merely $1 / 100$ training
set, demonstrating a tremendous data efficiency.
Fine-grained Apparel Classification and Retrieval without rich annotations
Authors: Aniket Bhatnagar, Sanchit Aggarwal
Date: 2018-11-06 00:00:00
Tasks: Classification, General Classification, Retrieval, Triplet

The ability to correctly classify and retrieve apparel images has a variety
of applications important to e-commerce, online advertising and internet
search. In this work, we propose a robust framework for fine-grained apparel
classification, in-shop and cross-domain retrieval which eliminates the
requirement of rich annotations like bounding boxes and human-joints or
clothing landmarks, and training of bounding box/ key-landmark detector for the
same. Factors such as subtle appearance differences, variations in human poses,
different shooting angles, apparel deformations, and self-occlusion add to the
challenges in classification and retrieval of apparel items. Cross-domain
retrieval is even harder due to the presence of large variation between online
shopping images, usually taken in ideal lighting, pose, positive angle and
clean background as compared with street photos captured by users in
complicated conditions with poor lighting and cluttered scenes. Our framework
uses compact bilinear CNN with tensor sketch algorithm to generate embeddings
that capture local pairwise feature interactions in a translationally invariant
manner. For apparel classification, we pass the feature embeddings through a
softmax classifier, while, the in-shop and cross-domain retrieval pipelines use
a triplet-loss based optimization approach, such that squared Euclidean
distance between embeddings measures the dissimilarity between the images.
Unlike previous works that relied on bounding box, key clothing landmarks or
human joint detectors to assist the final deep classifier, proposed framework
can be trained directly on the provided category labels or generated triplets
for triplet loss optimization. Lastly, Experimental results on the DeepFashion
fine-grained categorization, and in-shop and consumer-to-shop retrieval
datasets provide a comparative analysis with previous work performed in the
domain.
Robust Bhattacharyya bound linear discriminant analysis through adaptive algorithm
Authors: Chun-Na Li, Yuan-Hai Shao, Zhen Wang, Nai-Yang Deng
Date: 2018-11-06 00:00:00

In this paper, we propose a novel linear discriminant analysis criterion via
the Bhattacharyya error bound estimation based on a novel L1-norm (L1BLDA) and
L2-norm (L2BLDA). Both L1BLDA and L2BLDA maximize the between-class scatters
which are measured by the weighted pairwise distances of class means and
meanwhile minimize the within-class scatters under the L1-norm and L2-norm,
respectively. The proposed models can avoid the small sample size (SSS) problem
and have no rank limit that may encounter in LDA. It is worth mentioning that,
the employment of L1-norm gives a robust performance of L1BLDA, and L1BLDA is
solved through an effective non-greedy alternating direction method of
multipliers (ADMM), where all the projection vectors can be obtained once for
all. In addition, the weighting constants of L1BLDA and L2BLDA between the
between-class and within-class terms are determined by the involved data set,
which makes our L1BLDA and L2BLDA adaptive. The experimental results on both
benchmark data sets as well as the handwritten digit databases demonstrate the
effectiveness of the proposed methods.
Sets of autoencoders with shared latent spaces
Authors: Vasily Morzhakov
Date: 2018-11-06 00:00:00
Tasks: parameter estimation

Autoencoders receive latent models of input data. It was shown in recent
works that they also estimate probability density functions of the input. This
fact makes using the Bayesian decision theory possible. If we obtain latent
models of input data for each class or for some points in the space of
parameters in a parameter estimation task, we are able to estimate likelihood
functions for those classes or points in parameter space. We show how the set
of autoencoders solves the recognition problem. Each autoencoder describes its
own model or context, a latent vector that presents input data in the latent
space may be called treatment in its context. Sharing latent spaces of
autoencoders gives a very important property that is the ability to separate
treatment and context where the input information is treated through the set of
autoencoders. There are two remarkable and most valuable results of this work:
a mechanism that shows a possible way of forming abstract concepts and a way of
reducing dataset's size during training. These results are confirmed by tests
presented in the article.
Identificação automática de pichação a partir de imagens urbanas
Authors: Eric K. Tokuda, Claudio T. Silva, Roberto M. Cesar-Jr
Date: 2018-11-06 00:00:00

Graffiti tagging is a common issue in great cities an local authorities are
on the move to combat it. The tagging map of a city can be a useful tool as it
may help to clean-up highly saturated regions and discourage future acts in the
neighbourhood and currently there is no way of getting a tagging map of a
region in an automatic fashion and manual inspection or crowd participation are
required. In this work, we describe a work in progress in creating an automatic
way to get a tagging map of a city or region. It is based on the use of street
view images and on the detection of graffiti tags in the images.
A Description Logic Framework for Commonsense Conceptual Combination Integrating Typicality, Probabilities and Cognitive Heuristics
Authors: Antonio Lieto, Gian Luca Pozzato
Date: 2018-11-06 00:00:00

We propose a nonmonotonic Description Logic of typicality able to account for the phenomenon of concept combination of prototypical concepts. The proposed logic relies on the logic of typicality ALC TR, whose semantics is based on the notion of rational closure, as well as on the distributed semantics of probabilistic Description Logics, and is equipped with a cognitive heuristic used by humans for concept composition. We first extend the logic of typicality ALC TR by typicality inclusions whose intuitive meaning is that "there is probability p about the fact that typical Cs are Ds". As in the distributed semantics, we define different scenarios containing only some typicality inclusions, each one having a suitable probability. We then focus on those scenarios whose probabilities belong to a given and fixed range, and we exploit such scenarios in order to ascribe typical properties to a concept C obtained as the combination of two prototypical concepts. We also show that reasoning in the proposed Description Logic is EXPTIME-complete as for the underlying ALC.
Effective Subword Segmentation for Text Comprehension
Authors: Zhuosheng Zhang, Hai Zhao, Kangwei Ling, Jiangtong Li, Zuchao Li, Shexia He, Guohong Fu
Date: 2018-11-06 00:00:00
Tasks: Machine Reading Comprehension, Reading Comprehension, Representation Learning, Segmentation

Representation learning is the foundation of machine reading comprehension and inference. In state-of-the-art models, character-level representations have been broadly adopted to alleviate the problem of effectively representing rare or complex words. However, character itself is not a natural minimal linguistic unit for representation or word embedding composing due to ignoring the linguistic coherence of consecutive characters inside word. This paper presents a general subword-augmented embedding framework for learning and composing computationally-derived subword-level representations. We survey a series of unsupervised segmentation methods for subword acquisition and different subword-augmented strategies for text understanding, showing that subword-augmented embedding significantly improves our baselines in various types of text understanding tasks on both English and Chinese benchmarks.
Fast High-Dimensional Bilateral and Nonlocal Means Filtering
Authors: Pravin Nair, Kunal. N. Chaudhury
Date: 2018-11-06 00:00:00
Tasks: Clustering, Quantization, Vocal Bursts Intensity Prediction

Existing fast algorithms for bilateral and nonlocal means filtering mostly
work with grayscale images. They cannot easily be extended to high-dimensional
data such as color and hyperspectral images, patch-based data, flow-fields,
etc. In this paper, we propose a fast algorithm for high-dimensional bilateral
and nonlocal means filtering. Unlike existing approaches, where the focus is on
approximating the data (using quantization) or the filter kernel (via analytic
expansions), we locally approximate the kernel using weighted and shifted
copies of a Gaussian, where the weights and shifts are inferred from the data.
The algorithm emerging from the proposed approximation essentially involves
clustering and fast convolutions, and is easy to implement. Moreover, a variant
of our algorithm comes with a guarantee (bound) on the approximation error,
which is not enjoyed by existing algorithms. We present some results for
high-dimensional bilateral and nonlocal means filtering to demonstrate the
speed and accuracy of our proposal. Moreover, we also show that our algorithm
can outperform state-of-the-art fast approximations in terms of accuracy and
timing.
Kalman Filter Modifier for Neural Networks in Non-stationary Environments
Authors: Honglin Li, Frieder Ganz, Shirin Enshaeifar, Payam Barnaghi
Date: 2018-11-06 00:00:00
Tasks: BIG-bench Machine Learning

Learning in a non-stationary environment is an inevitable problem when
applying machine learning algorithm to real world environment. Learning new
tasks without forgetting the previous knowledge is a challenge issue in machine
learning. We propose a Kalman Filter based modifier to maintain the performance
of Neural Network models under non-stationary environments. The result shows
that our proposed model can preserve the key information and adapts better to
the changes. The accuracy of proposed model decreases by 0.4% in our
experiments, while the accuracy of conventional model decreases by 90% in the
drifts environment.
Micro-Attention for Micro-Expression recognition
Authors: Chongyang Wang, Min Peng, Tao Bi, Tong Chen
Date: 2018-11-06 00:00:00
Tasks: Micro Expression Recognition, Micro-Expression Recognition, Transfer Learning

Micro-expression, for its high objectivity in emotion detection, has emerged to be a promising modality in affective computing. Recently, deep learning methods have been successfully introduced into the micro-expression recognition area. Whilst the higher recognition accuracy achieved, substantial challenges in micro-expression recognition remain. The existence of micro expression in small-local areas on face and limited size of available databases still constrain the recognition accuracy on such emotional facial behavior. In this work, to tackle such challenges, we propose a novel attention mechanism called micro-attention cooperating with residual network. Micro-attention enables the network to learn to focus on facial areas of interest covering different action units. Moreover, coping with small datasets, the micro-attention is designed without adding noticeable parameters while a simple yet efficient transfer learning approach is together utilized to alleviate the overfitting risk. With extensive experimental evaluations on three benchmarks (CASMEII, SAMM and SMIC) and post-hoc feature visualizations, we demonstrate the effectiveness of the proposed micro-attention and push the boundary of automatic recognition of micro-expression.
Object 3D Reconstruction based on Photometric Stereo and Inverted Rendering
Authors: Anish R. Khadka, Paolo Remagnino, Vasileios Argyriou
Date: 2018-11-06 00:00:00
Tasks: 3D Reconstruction

Methods for 3D reconstruction such as Photometric stereo recover the shape
and reflectance properties using multiple images of an object taken with
variable lighting conditions from a fixed viewpoint. Photometric stereo assumes
that a scene is illuminated only directly by the illumination source. As
result, indirect illumination effects due to inter-reflections introduce strong
biases in the recovered shape. Our suggested approach is to recover scene
properties in the presence of indirect illumination. To this end, we proposed
an iterative PS method combined with a reverted Monte-Carlo ray tracing
algorithm to overcome the inter-reflection effects aiming to separate the
direct and indirect lighting. This approach iteratively reconstructs a surface
considering both the environment around the object and its concavities. We
demonstrate and evaluate our approach using three datasets and the overall
results illustrate improvement over the classic PS approaches.
Code-switching Sentence Generation by Generative Adversarial Networks and its Application to Data Augmentation
Authors: Ching-Ting Chang, Shun-Po Chuang, Hung-Yi Lee
Date: 2018-11-06 00:00:00
Tasks: Data Augmentation, Generative Adversarial Network, Sentence

Code-switching is about dealing with alternative languages in speech or text. It is partially speaker-depend and domain-related, so completely explaining the phenomenon by linguistic rules is challenging. Compared to most monolingual tasks, insufficient data is an issue for code-switching. To mitigate the issue without expensive human annotation, we proposed an unsupervised method for code-switching data augmentation. By utilizing a generative adversarial network, we can generate intra-sentential code-switching sentences from monolingual sentences. We applied proposed method on two corpora, and the result shows that the generated code-switching sentences improve the performance of code-switching language models.
Learning to Embed Sentences Using Attentive Recursive Trees
Authors: Jiaxin Shi, Lei Hou, Juanzi Li, Zhiyuan Liu, Hanwang Zhang
Date: 2018-11-06 00:00:00
Tasks: Sentence, Sentence Embedding, Sentence-Embedding

Sentence embedding is an effective feature representation for most deep
learning-based NLP tasks. One prevailing line of methods is using recursive
latent tree-structured networks to embed sentences with task-specific
structures. However, existing models have no explicit mechanism to emphasize
task-informative words in the tree structure. To this end, we propose an
Attentive Recursive Tree model (AR-Tree), where the words are dynamically
located according to their importance in the task. Specifically, we construct
the latent tree for a sentence in a proposed important-first strategy, and
place more attentive words nearer to the root; thus, AR-Tree can inherently
emphasize important words during the bottom-up composition of the sentence
embedding. We propose an end-to-end reinforced training strategy for AR-Tree,
which is demonstrated to consistently outperform, or be at least comparable to,
the state-of-the-art sentence embedding methods on three sentence understanding
tasks.
Super-Identity Convolutional Neural Network for Face Hallucination
Authors: Kaipeng Zhang, Zhanpeng Zhang, Chia-Wen Cheng, Winston H. Hsu, Yu Qiao, Wei Liu, Tong Zhang
Date: 2018-11-06 00:00:00
Proceeding: ECCV 2018 9
Tasks: Face Generation, Face Hallucination, Hallucination

Face hallucination is a generative task to super-resolve the facial image
with low resolution while human perception of face heavily relies on identity
information. However, previous face hallucination approaches largely ignore
facial identity recovery. This paper proposes Super-Identity Convolutional
Neural Network (SICNN) to recover identity information for generating faces
closed to the real identity. Specifically, we define a super-identity loss to
measure the identity difference between a hallucinated face and its
corresponding high-resolution face within the hypersphere identity metric
space. However, directly using this loss will lead to a Dynamic Domain
Divergence problem, which is caused by the large margin between the
high-resolution domain and the hallucination domain. To overcome this
challenge, we present a domain-integrated training approach by constructing a
robust identity metric for faces from these two domains. Extensive experimental
evaluations demonstrate that the proposed SICNN achieves superior visual
quality over the state-of-the-art methods on a challenging task to
super-resolve 12$\times$14 faces with an 8$\times$ upscaling factor. In
addition, SICNN significantly improves the recognizability of
ultra-low-resolution faces.
Elastic CoCoA: Scaling In to Improve Convergence
Authors: Michael Kaufmann, Thomas Parnell, Kornilios Kourtis
Date: 2018-11-06 00:00:00

In this paper we experimentally analyze the convergence behavior of CoCoA and
show, that the number of workers required to achieve the highest convergence
rate at any point in time, changes over the course of the training. Based on
this observation, we build Chicle, an elastic framework that dynamically
adjusts the number of workers based on feedback from the training algorithm, in
order to select the number of workers that results in the highest convergence
rate. In our evaluation of 6 datasets, we show that Chicle is able to
accelerate the time-to-accuracy by a factor of up to 5.96x compared to the best
static setting, while being robust enough to find an optimal or near-optimal
setting automatically in most cases.
Hierarchical Neural Network Architecture In Keyword Spotting
Authors: Yixiao Qu, Sihao Xue, Zhenyi Ying, Hang Zhou, Jue Sun
Date: 2018-11-06 00:00:00
Tasks: Keyword Spotting, speech-recognition, Speech Recognition

Keyword Spotting (KWS) provides the start signal of ASR problem, and thus it
is essential to ensure a high recall rate. However, its real-time property
requires low computation complexity. This contradiction inspires people to find
a suitable model which is small enough to perform well in multi environments.
To deal with this contradiction, we implement the Hierarchical Neural
Network(HNN), which is proved to be effective in many speech recognition
problems. HNN outperforms traditional DNN and CNN even though its model size
and computation complexity are slightly less. Also, its simple topology
structure makes easy to deploy on any device.