pwc-archive/papers-with-abstracts huggingface dataset:
Tensorizing Generative Adversarial Nets
Authors: Xingwei Cao, Xuyang Zhao, Qibin Zhao
Date: 2017-10-30 00:00:00
Tasks: Generative Adversarial Network

Generative Adversarial Network (GAN) and its variants exhibit
state-of-the-art performance in the class of generative models. To capture
higher-dimensional distributions, the common learning procedure requires high
computational complexity and a large number of parameters. The problem of
employing such massive framework arises when deploying it on a platform with
limited computational power such as mobile phones. In this paper, we present a
new generative adversarial framework by representing each layer as a tensor
structure connected by multilinear operations, aiming to reduce the number of
model parameters by a large factor while preserving the generative performance
and sample quality. To learn the model, we employ an efficient algorithm which
alternatively optimizes both discriminator and generator. Experimental outcomes
demonstrate that our model can achieve high compression rate for model
parameters up to $35$ times when compared to the original GAN for MNIST
dataset.
Transductive Unbiased Embedding for Zero-Shot Learning
Authors: Jie Song, Chengchao Shen, Yezhou Yang, Yang Liu, Mingli Song
Date: 2018-03-30 00:00:00
Proceeding: CVPR 2018 6
Tasks: Transductive Learning, Zero-Shot Learning

Most existing Zero-Shot Learning (ZSL) methods have the strong bias problem,
in which instances of unseen (target) classes tend to be categorized as one of
the seen (source) classes. So they yield poor performance after being deployed
in the generalized ZSL settings. In this paper, we propose a straightforward
yet effective method named Quasi-Fully Supervised Learning (QFSL) to alleviate
the bias problem. Our method follows the way of transductive learning, which
assumes that both the labeled source images and unlabeled target images are
available for training. In the semantic embedding space, the labeled source
images are mapped to several fixed points specified by the source categories,
and the unlabeled target images are forced to be mapped to other points
specified by the target categories. Experiments conducted on AwA2, CUB and SUN
datasets demonstrate that our method outperforms existing state-of-the-art
approaches by a huge margin of 9.3~24.5% following generalized ZSL settings,
and by a large margin of 0.2~16.2% following conventional ZSL settings.
Task-Driven Super Resolution: Object Detection in Low-resolution Images
Authors: Muhammad Haris, Greg Shakhnarovich, Norimichi Ukita
Date: 2018-03-30 00:00:00
Tasks: Image Super-Resolution, Object, object-detection, Object Detection, Super-Resolution

We consider how image super resolution (SR) can contribute to an object
detection task in low-resolution images. Intuitively, SR gives a positive
impact on the object detection task. While several previous works demonstrated
that this intuition is correct, SR and detector are optimized independently in
these works. This paper proposes a novel framework to train a deep neural
network where the SR sub-network explicitly incorporates a detection loss in
its training objective, via a tradeoff with a traditional detection loss. This
end-to-end training procedure allows us to train SR preprocessing for any
differentiable detector. We demonstrate that our task-driven SR consistently
and significantly improves accuracy of an object detector on low-resolution
images for a variety of conditions and scaling factors.
Separating Style and Content for Generalized Style Transfer
Authors: Yexun Zhang, Ya zhang, Wenbin Cai, Jie Chang
Date: 2017-11-17 00:00:00
Proceeding: CVPR 2018 6
Tasks: Decoder, Multi-Task Learning, Style Transfer

Neural style transfer has drawn broad attention in recent years. However,
most existing methods aim to explicitly model the transformation between
different styles, and the learned model is thus not generalizable to new
styles. We here attempt to separate the representations for styles and
contents, and propose a generalized style transfer network consisting of style
encoder, content encoder, mixer and decoder. The style encoder and content
encoder are used to extract the style and content factors from the style
reference images and content reference images, respectively. The mixer employs
a bilinear model to integrate the above two factors and finally feeds it into a
decoder to generate images with target style and content. To separate the style
features and content features, we leverage the conditional dependence of styles
and contents given an image. During training, the encoder network learns to
extract styles and contents from two sets of reference images in limited size,
one with shared style and the other with shared content. This learning
framework allows simultaneous style transfer among multiple styles and can be
deemed as a special `multi-task' learning scenario. The encoders are expected
to capture the underlying features for different styles and contents which is
generalizable to new styles and contents. For validation, we applied the
proposed algorithm to the Chinese Typeface transfer problem. Extensive
experiment results on character generation have demonstrated the effectiveness
and robustness of our method.
Audio Adversarial Examples: Targeted Attacks on Speech-to-Text
Authors: Nicholas Carlini, David Wagner
Date: 2018-01-05 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Speech-to-Text

We construct targeted audio adversarial examples on automatic speech
recognition. Given any audio waveform, we can produce another that is over
99.9% similar, but transcribes as any phrase we choose (recognizing up to 50
characters per second of audio). We apply our white-box iterative
optimization-based attack to Mozilla's implementation DeepSpeech end-to-end,
and show it has a 100% success rate. The feasibility of this attack introduce a
new domain to study adversarial examples.
Relaxed Wasserstein with Applications to GANs
Authors: Xin Guo, Johnny Hong, Tianyi Lin, Nan Yang
Date: 2017-05-19 00:00:00
Tasks: Image Generation

Wasserstein Generative Adversarial Networks (WGANs) provide a versatile class of models, which have attracted great attention in various applications. However, this framework has two main drawbacks: (i) Wasserstein-1 (or Earth-Mover) distance is restrictive such that WGANs cannot always fit data geometry well; (ii) It is difficult to achieve fast training of WGANs. In this paper, we propose a new class of \textit{Relaxed Wasserstein} (RW) distances by generalizing Wasserstein-1 distance with Bregman cost functions. We show that RW distances achieve nice statistical properties while not sacrificing the computational tractability. Combined with the GANs framework, we develop Relaxed WGANs (RWGANs) which are not only statistically flexible but can be approximated efficiently using heuristic approaches. Experiments on real images demonstrate that the RWGAN with Kullback-Leibler (KL) cost function outperforms other competing approaches, e.g., WGANs, even with gradient penalty.
Robust Subspace Clustering with Compressed Data
Authors: Guangcan Liu, Zhao Zhang, Qingshan Liu, Kongkai Xiong
Date: 2018-03-30 00:00:00
Tasks: Clustering, Computational Efficiency, Dimensionality Reduction

Dimension reduction is widely regarded as an effective way for decreasing the computation, storage and communication loads of data-driven intelligent systems, leading to a growing demand for statistical methods that allow analysis (e.g., clustering) of compressed data. We therefore study in this paper a novel problem called compressive robust subspace clustering, which is to perform robust subspace clustering with the compressed data, and which is generated by projecting the original high-dimensional data onto a lower-dimensional subspace chosen at random. Given only the compressed data and sensing matrix, the proposed method, row space pursuit (RSP), recovers the authentic row space that gives correct clustering results under certain conditions. Extensive experiments show that RSP is distinctly better than the competing methods, in terms of both clustering accuracy and computational efficiency.
Pancreas Segmentation in CT and MRI Images via Domain Specific Network Designing and Recurrent Neural Contextual Learning
Authors: Jinzheng Cai, Le Lu, Fuyong Xing, Lin Yang
Date: 2018-03-30 00:00:00
Tasks: Computed Tomography (CT), Pancreas Segmentation, Segmentation

Automatic pancreas segmentation in radiology images, eg., computed tomography
(CT) and magnetic resonance imaging (MRI), is frequently required by
computer-aided screening, diagnosis, and quantitative assessment. Yet pancreas
is a challenging abdominal organ to segment due to the high inter-patient
anatomical variability in both shape and volume metrics. Recently,
convolutional neural networks (CNNs) have demonstrated promising performance on
accurate segmentation of pancreas. However, the CNN-based method often suffers
from segmentation discontinuity for reasons such as noisy image quality and
blurry pancreatic boundary. From this point, we propose to introduce recurrent
neural networks (RNNs) to address the problem of spatial non-smoothness of
inter-slice pancreas segmentation across adjacent image slices. To inference
initial segmentation, we first train a 2D CNN sub-network, where we modify its
network architecture with deep-supervision and multi-scale feature map
aggregation so that it can be trained from scratch with small-sized training
data and presents superior performance than transferred models. Thereafter, the
successive CNN outputs are processed by another RNN sub-network, which refines
the consistency of segmented shapes. More specifically, the RNN sub-network
consists convolutional long short-term memory (CLSTM) units in both top-down
and bottom-up directions, which regularizes the segmentation of an image by
integrating predictions of its neighboring slices. We train the stacked CNN-RNN
model end-to-end and perform quantitative evaluations on both CT and MRI
images.
On spectral partitioning of signed graphs
Authors: Andrew V. Knyazev
Date: 2017-01-05 00:00:00

We argue that the standard graph Laplacian is preferable for spectral
partitioning of signed graphs compared to the signed Laplacian. Simple examples
demonstrate that partitioning based on signs of components of the leading
eigenvectors of the signed Laplacian may be meaningless, in contrast to
partitioning based on the Fiedler vector of the standard graph Laplacian for
signed graphs. We observe that negative eigenvalues are beneficial for spectral
partitioning of signed graphs, making the Fiedler vector easier to compute.
Structural Risk Minimization for $C^{1,1}(\mathbb{R}^d)$ Regression
Authors: Adam Gustafson, Matthew Hirn, Kitty Mohammed, Hariharan Narayanan, Jason Xu
Date: 2018-03-29 00:00:00
Tasks: regression

One means of fitting functions to high-dimensional data is by providing
smoothness constraints. Recently, the following smooth function approximation
problem was proposed: given a finite set $E \subset \mathbb{R}^d$ and a
function $f: E \rightarrow \mathbb{R}$, interpolate the given information with
a function $\widehat{f} \in \dot{C}^{1, 1}(\mathbb{R}^d)$ (the class of
first-order differentiable functions with Lipschitz gradients) such that
$\widehat{f}(a) = f(a)$ for all $a \in E$, and the value of
$\mathrm{Lip}(\nabla \widehat{f})$ is minimal. An algorithm is provided that
constructs such an approximating function $\widehat{f}$ and estimates the
optimal Lipschitz constant $\mathrm{Lip}(\nabla \widehat{f})$ in the noiseless
setting.
  We address statistical aspects of reconstructing the approximating function
$\widehat{f}$ from a closely-related class $C^{1, 1}(\mathbb{R}^d)$ given
samples from noisy data. We observe independent and identically distributed
samples $y(a) = f(a) + \xi(a)$ for $a \in E$, where $\xi(a)$ is a noise term
and the set $E \subset \mathbb{R}^d$ is fixed and known. We obtain uniform
bounds relating the empirical risk and true risk over the class
$\mathcal{F}_{\widetilde{M}} = \{f \in C^{1, 1}(\mathbb{R}^d) \mid
\mathrm{Lip}(\nabla f) \leq \widetilde{M}\}$, where the quantity
$\widetilde{M}$ grows with the number of samples at a rate governed by the
metric entropy of the class $C^{1, 1}(\mathbb{R}^d)$. Finally, we provide an
implementation using Vaidya's algorithm, supporting our results via numerical
experiments on simulated data.
Deep learning-based virtual histology staining using auto-fluorescence of label-free tissue
Authors: Yair Rivenson, Hongda Wang, Zhensong Wei, Yibo Zhang, Harun Gunaydin, Aydogan Ozcan
Date: 2018-03-30 00:00:00
Tasks: Generative Adversarial Network, Virtual Staining

Histological analysis of tissue samples is one of the most widely used
methods for disease diagnosis. After taking a sample from a patient, it goes
through a lengthy and laborious preparation, which stains the tissue to
visualize different histological features under a microscope. Here, we
demonstrate a label-free approach to create a virtually-stained microscopic
image using a single wide-field auto-fluorescence image of an unlabeled tissue
sample, bypassing the standard histochemical staining process, saving time and
cost. This method is based on deep learning, and uses a convolutional neural
network trained using a generative adversarial network model to transform an
auto-fluorescence image of an unlabeled tissue section into an image that is
equivalent to the bright-field image of the stained-version of the same sample.
We validated this method by successfully creating virtually-stained microscopic
images of human tissue samples, including sections of salivary gland, thyroid,
kidney, liver and lung tissue, also covering three different stains. This
label-free virtual-staining method eliminates cumbersome and costly
histochemical staining procedures, and would significantly simplify tissue
preparation in pathology and histology fields.
Robust Cross-lingual Hypernymy Detection using Dependency Context
Authors: Shyam Upadhyay, Yogarshi Vyas, Marine Carpuat, Dan Roth
Date: 2018-03-30 00:00:00
Proceeding: NAACL 2018 6
Tasks: Natural Language Inference, Word Embeddings

Cross-lingual Hypernymy Detection involves determining if a word in one
language ("fruit") is a hypernym of a word in another language ("pomme" i.e.
apple in French). The ability to detect hypernymy cross-lingually can aid in
solving cross-lingual versions of tasks such as textual entailment and event
coreference. We propose BISPARSE-DEP, a family of unsupervised approaches for
cross-lingual hypernymy detection, which learns sparse, bilingual word
embeddings based on dependency contexts. We show that BISPARSE-DEP can
significantly improve performance on this task, compared to approaches based
only on lexical context. Our approach is also robust, showing promise for
low-resource settings: our dependency-based embeddings can be learned using a
parser trained on related languages, with negligible loss in performance. We
also crowd-source a challenging dataset for this task on four languages --
Russian, French, Arabic, and Chinese. Our embeddings and datasets are publicly
available.
FutureMapping: The Computational Structure of Spatial AI Systems
Authors: Andrew J. Davison
Date: 2018-03-29 00:00:00

We discuss and predict the evolution of Simultaneous Localisation and Mapping
(SLAM) into a general geometric and semantic `Spatial AI' perception capability
for intelligent embodied devices. A big gap remains between the visual
perception performance that devices such as augmented reality eyewear or
comsumer robots will require and what is possible within the constraints
imposed by real products. Co-design of algorithms, processors and sensors will
be needed. We explore the computational structure of current and future Spatial
AI algorithms and consider this within the landscape of ongoing hardware
developments.
A Stochastic Large-scale Machine Learning Algorithm for Distributed Features and Observations
Authors: Biyi Fang, Diego Klabjan
Date: 2018-03-29 00:00:00
Tasks: BIG-bench Machine Learning, Distributed Computing, Distributed Optimization

As the size of modern data sets exceeds the disk and memory capacities of a single computer, machine learning practitioners have resorted to parallel and distributed computing. Given that optimization is one of the pillars of machine learning and predictive modeling, distributed optimization methods have recently garnered ample attention, in particular when either observations or features are distributed, but not both. We propose a general stochastic algorithm where observations, features, and gradient components can be sampled in a double distributed setting, i.e., with both features and observations distributed. Very technical analyses establish convergence properties of the algorithm under different conditions on the learning rate (diminishing to zero or constant). Computational experiments in Spark demonstrate a superior performance of our algorithm versus a benchmark in early iterations of the algorithm, which is due to the stochastic components of the algorithm.
Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking
Authors: Filip Radenović, Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, Ondřej Chum
Date: 2018-03-29 00:00:00
Proceeding: CVPR 2018 6
Tasks: Benchmarking, Image Retrieval, Retrieval

In this paper we address issues with image retrieval benchmarking on standard
and popular Oxford 5k and Paris 6k datasets. In particular, annotation errors,
the size of the dataset, and the level of challenge are addressed: new
annotation for both datasets is created with an extra attention to the
reliability of the ground truth. Three new protocols of varying difficulty are
introduced. The protocols allow fair comparison between different methods,
including those using a dataset pre-processing stage. For each dataset, 15 new
challenging queries are introduced. Finally, a new set of 1M hard,
semi-automatically cleaned distractors is selected.
  An extensive comparison of the state-of-the-art methods is performed on the
new benchmark. Different types of methods are evaluated, ranging from
local-feature-based to modern CNN based methods. The best results are achieved
by taking the best of the two worlds. Most importantly, image retrieval appears
far from being solved.
Extreme 3D Face Reconstruction: Seeing Through Occlusions
Authors: Anh Tuan Tran, Tal Hassner, Iacopo Masi, Eran Paz, Yuval Nirkin, Gerard Medioni
Date: 2017-12-14 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D Face Reconstruction, Decoder, Face Reconstruction

Existing single view, 3D face reconstruction methods can produce beautifully
detailed 3D results, but typically only for near frontal, unobstructed
viewpoints. We describe a system designed to provide detailed 3D
reconstructions of faces viewed under extreme conditions, out of plane
rotations, and occlusions. Motivated by the concept of bump mapping, we propose
a layered approach which decouples estimation of a global shape from its
mid-level details (e.g., wrinkles). We estimate a coarse 3D face shape which
acts as a foundation and then separately layer this foundation with details
represented by a bump map. We show how a deep convolutional encoder-decoder can
be used to estimate such bump maps. We further show how this approach naturally
extends to generate plausible details for occluded facial regions. We test our
approach and its components extensively, quantitatively demonstrating the
invariance of our estimated facial details. We further provide numerous
qualitative examples showing that our method produces detailed 3D face shapes
in viewing conditions where existing state of the art often break down.
Deep Recurrent Neural Networks for Product Attribute Extraction in eCommerce
Authors: Bodhisattwa Prasad Majumder, Aditya Subramanian, Abhinandan Krishnan, Shreyansh Gandhi, Ajinkya More
Date: 2018-03-29 00:00:00
Tasks: Attribute, Attribute Extraction

Extracting accurate attribute qualities from product titles is a vital
component in delivering eCommerce customers with a rewarding online shopping
experience via an enriched faceted search. We demonstrate the potential of Deep
Recurrent Networks in this domain, primarily models such as Bidirectional LSTMs
and Bidirectional LSTM-CRF with or without an attention mechanism. These have
improved overall F1 scores, as compared to the previous benchmarks (More et
al.) by at least 0.0391, showcasing an overall precision of 97.94%, recall of
94.12% and the F1 score of 0.9599. This has made us achieve a significant
coverage of important facets or attributes of products which not only shows the
efficacy of deep recurrent models over previous machine learning benchmarks but
also greatly enhances the overall customer experience while shopping online.
Two-Stream Neural Networks for Tampered Face Detection
Authors: Peng Zhou, Xintong Han, Vlad I. Morariu, Larry S. Davis
Date: 2018-03-29 00:00:00
Tasks: Face Detection, Face Swapping, General Classification, Triplet, Vocal Bursts Valence Prediction

We propose a two-stream network for face tampering detection. We train
GoogLeNet to detect tampering artifacts in a face classification stream, and
train a patch based triplet network to leverage features capturing local noise
residuals and camera characteristics as a second stream. In addition, we use
two different online face swapping applications to create a new dataset that
consists of 2010 tampered images, each of which contains a tampered face. We
evaluate the proposed two-stream network on our newly collected dataset.
Experimental results demonstrate the effectiveness of our method.