pwc-archive/papers-with-abstracts huggingface dataset:
Learning Image Representations by Completing Damaged Jigsaw Puzzles
Authors: Dahun Kim, Donghyeon Cho, Donggeun Yoo, In So Kweon
Date: 2018-02-06 00:00:00
Tasks: Colorization, Representation Learning, Semantic Segmentation, Transfer Learning

In this paper, we explore methods of complicating self-supervised tasks for
representation learning. That is, we do severe damage to data and encourage a
network to recover them. First, we complicate each of three powerful
self-supervised task candidates: jigsaw puzzle, inpainting, and colorization.
In addition, we introduce a novel complicated self-supervised task called
"Completing damaged jigsaw puzzles" which is puzzles with one piece missing and
the other pieces without color. We train a convolutional neural network not
only to solve the puzzles, but also generate the missing content and colorize
the puzzles. The recovery of the aforementioned damage pushes the network to
obtain robust and general-purpose representations. We demonstrate that
complicating the self-supervised tasks improves their original versions and
that our final task learns more robust and transferable representations
compared to the previous methods, as well as the simple combination of our
candidate tasks. Our approach achieves state-of-the-art performance in transfer
learning on PASCAL classification and semantic segmentation.
Fast Piecewise-Affine Motion Estimation Without Segmentation
Authors: Denis Fortun, Martin Storath, Dennis Rickert, Andreas Weinmann, Michael Unser
Date: 2018-02-06 00:00:00
Tasks: Motion Estimation, Motion Segmentation, Segmentation

Current algorithmic approaches for piecewise affine motion estimation are
based on alternating motion segmentation and estimation. We propose a new
method to estimate piecewise affine motion fields directly without intermediate
segmentation. To this end, we reformulate the problem by imposing piecewise
constancy of the parameter field, and derive a specific proximal splitting
optimization scheme. A key component of our framework is an efficient
one-dimensional piecewise-affine estimator for vector-valued signals. The first
advantage of our approach over segmentation-based methods is its absence of
initialization. The second advantage is its lower computational cost which is
independent of the complexity of the motion field. In addition to these
features, we demonstrate competitive accuracy with other piecewise-parametric
methods on standard evaluation benchmarks. Our new regularization scheme also
outperforms the more standard use of total variation and total generalized
variation.
Trace norm regularization and faster inference for embedded speech recognition RNNs
Authors: Markus Kliegl, Siddharth Goyal, Kexin Zhao, Kavya Srinet, Mohammad Shoeybi
Date: 2017-10-25 00:00:00
Proceeding: ICLR 2018 1
Tasks: speech-recognition, Speech Recognition

We propose and evaluate new techniques for compressing and speeding up dense
matrix multiplications as found in the fully connected and recurrent layers of
neural networks for embedded large vocabulary continuous speech recognition
(LVCSR). For compression, we introduce and study a trace norm regularization
technique for training low rank factored versions of matrix multiplications.
Compared to standard low rank training, we show that our method leads to good
accuracy versus number of parameter trade-offs and can be used to speed up
training of large models. For speedup, we enable faster inference on ARM
processors through new open sourced kernels optimized for small batch sizes,
resulting in 3x to 7x speed ups over the widely used gemmlowp library. Beyond
LVCSR, we expect our techniques and kernels to be more generally applicable to
embedded neural networks with large fully connected or recurrent layers.
Search Based Code Generation for Machine Learning Programs
Authors: Muhammad Zubair Malik, Muhammad Nawaz, Nimrah Mustafa, Junaid Haroon Siddiqui
Date: 2018-01-29 00:00:00
Tasks: BIG-bench Machine Learning, Code Generation

Machine Learning (ML) has revamped every domain of life as it provides
powerful tools to build complex systems that learn and improve from experience
and data. Our key insight is that to solve a machine learning problem, data
scientists do not invent a new algorithm each time, but evaluate a range of
existing models with different configurations and select the best one. This
task is laborious, error-prone, and drains a large chunk of project budget and
time. In this paper we present a novel framework inspired by programming by
Sketching and Partial Evaluation to minimize human intervention in developing
ML solutions. We templatize machine learning algorithms to expose configuration
choices as holes to be searched. We share code and computation between
different algorithms, and only partially evaluate configuration space of
algorithms based on information gained from initial algorithm evaluations. We
also employ hierarchical and heuristic based pruning to reduce the search
space. Our initial findings indicate that our approach can generate highly
accurate ML models. Interviews with data scientists show that they feel our
framework can eliminate sources of common errors and significantly reduce
development time.
Adversarial Attribute-Image Person Re-identification
Authors: Zhou Yin, Wei-Shi Zheng, An-Cong Wu, Hong-Xing Yu, Hai Wan, Xiaowei Guo, Feiyue Huang, Jian-Huang Lai
Date: 2017-12-05 00:00:00
Tasks: Attribute, Multi-Task Learning, Person Re-Identification

While attributes have been widely used for person re-identification (Re-ID)
which aims at matching the same person images across disjoint camera views,
they are used either as extra features or for performing multi-task learning to
assist the image-image matching task. However, how to find a set of person
images according to a given attribute description, which is very practical in
many surveillance applications, remains a rarely investigated cross-modality
matching problem in person Re-ID. In this work, we present this challenge and
formulate this task as a joint space learning problem. By imposing an
attribute-guided attention mechanism for images and a semantic consistent
adversary strategy for attributes, each modality, i.e., images and attributes,
successfully learns semantically correlated concepts under the guidance of the
other. We conducted extensive experiments on three attribute datasets and
demonstrated that the proposed joint space learning method is so far the most
effective method for the attribute-image cross-modality person Re-ID problem.
A Neurobiologically Motivated Analysis of Distributional Semantic Models
Authors: Akira Utsumi
Date: 2018-02-06 00:00:00
Tasks: Word Embeddings

The pervasive use of distributional semantic models or word embeddings in a
variety of research fields is due to their remarkable ability to represent the
meanings of words for both practical application and cognitive modeling.
However, little has been known about what kind of information is encoded in
text-based word vectors. This lack of understanding is particularly problematic
when word vectors are regarded as a model of semantic representation for
abstract concepts. This paper attempts to reveal the internal information of
distributional word vectors by the analysis using Binder et al.'s (2016)
brain-based vectors, explicitly structured conceptual representations based on
neurobiologically motivated attributes. In the analysis, the mapping from
text-based vectors to brain-based vectors is trained and prediction performance
is evaluated by comparing the estimated and original brain-based vectors. The
analysis demonstrates that social and cognitive information is better encoded
in text-based word vectors, but emotional information is not. This result is
discussed in terms of embodied theories for abstract concepts.
Geometry-Contrastive GAN for Facial Expression Transfer
Authors: Fengchun Qiao, Naiming Yao, Zirui Jiao, Zhihao LI, Hui Chen, Hongan Wang
Date: 2018-02-06 00:00:00
Tasks: Contrastive Learning, Generative Adversarial Network

In this paper, we propose a Geometry-Contrastive Generative Adversarial
Network (GC-GAN) for transferring continuous emotions across different
subjects. Given an input face with certain emotion and a target facial
expression from another subject, GC-GAN can generate an identity-preserving
face with the target expression. Geometry information is introduced into cGANs
as continuous conditions to guide the generation of facial expressions. In
order to handle the misalignment across different subjects or emotions,
contrastive learning is used to transform geometry manifold into an embedded
semantic manifold of facial expressions. Therefore, the embedded geometry is
injected into the latent space of GANs and control the emotion generation
effectively. Experimental results demonstrate that our proposed method can be
applied in facial expression transfer even there exist big differences in
facial shapes and expressions between different subjects.
Byte-Level Recursive Convolutional Auto-Encoder for Text
Authors: Xiang Zhang, Yann Lecun
Date: 2018-02-06 00:00:00
Proceeding: ICLR 2018 1
Tasks: Decoder, Text Generation

This article proposes to auto-encode text at byte-level using convolutional
networks with a recursive architecture. The motivation is to explore whether it
is possible to have scalable and homogeneous text generation at byte-level in a
non-sequential fashion through the simple task of auto-encoding. We show that
non-sequential text generation from a fixed-length representation is not only
possible, but also achieved much better auto-encoding results than recurrent
networks. The proposed model is a multi-stage deep convolutional
encoder-decoder framework using residual connections, containing up to 160
parameterized layers. Each encoder or decoder contains a shared group of
modules that consists of either pooling or upsampling layers, making the
network recursive in terms of abstraction levels in representation. Results for
6 large-scale paragraph datasets are reported, in 3 languages including Arabic,
Chinese and English. Analyses are conducted to study several properties of the
proposed model.
Decoding-History-Based Adaptive Control of Attention for Neural Machine Translation
Authors: Junyang Lin, Shuming Ma, Qi Su, Xu sun
Date: 2018-02-06 00:00:00
Tasks: Decoder, Machine Translation, NMT, Translation

Attention-based sequence-to-sequence model has proved successful in Neural
Machine Translation (NMT). However, the attention without consideration of
decoding history, which includes the past information in the decoder and the
attention mechanism, often causes much repetition. To address this problem, we
propose the decoding-history-based Adaptive Control of Attention (ACA) for the
NMT model. ACA learns to control the attention by keeping track of the decoding
history and the current information with a memory vector, so that the model can
take the translated contents and the current information into consideration.
Experiments on Chinese-English translation and the English-Vietnamese
translation have demonstrated that our model significantly outperforms the
strong baselines. The analysis shows that our model is capable of generating
translation with less repetition and higher accuracy. The code will be
available at https://github.com/lancopku
Mixed Link Networks
Authors: Wenhai Wang, Xiang Li, Jian Yang, Tong Lu
Date: 2018-02-06 00:00:00
Tasks: Representation Learning

Basing on the analysis by revealing the equivalence of modern networks, we
find that both ResNet and DenseNet are essentially derived from the same "dense
topology", yet they only differ in the form of connection -- addition (dubbed
"inner link") vs. concatenation (dubbed "outer link"). However, both two forms
of connections have the superiority and insufficiency. To combine their
advantages and avoid certain limitations on representation learning, we present
a highly efficient and modularized Mixed Link Network (MixNet) which is
equipped with flexible inner link and outer link modules. Consequently, ResNet,
DenseNet and Dual Path Network (DPN) can be regarded as a special case of
MixNet, respectively. Furthermore, we demonstrate that MixNets can achieve
superior efficiency in parameter over the state-of-the-art architectures on
many competitive datasets like CIFAR-10/100, SVHN and ImageNet.
Digital Watermarking for Deep Neural Networks
Authors: Yuki Nagai, Yusuke Uchida, Shigeyuki Sakazawa, Shin'ichi Satoh
Date: 2018-02-06 00:00:00

Although deep neural networks have made tremendous progress in the area of
multimedia representation, training neural models requires a large amount of
data and time. It is well-known that utilizing trained models as initial
weights often achieves lower training error than neural networks that are not
pre-trained. A fine-tuning step helps to reduce both the computational cost and
improve performance. Therefore, sharing trained models has been very important
for the rapid progress of research and development. In addition, trained models
could be important assets for the owner(s) who trained them, hence we regard
trained models as intellectual property. In this paper, we propose a digital
watermarking technology for ownership authorization of deep neural networks.
First, we formulate a new problem: embedding watermarks into deep neural
networks. We also define requirements, embedding situations, and attack types
on watermarking in deep neural networks. Second, we propose a general framework
for embedding a watermark in model parameters, using a parameter regularizer.
Our approach does not impair the performance of networks into which a watermark
is placed because the watermark is embedded while training the host network.
Finally, we perform comprehensive experiments to reveal the potential of
watermarking deep neural networks as the basis of this new research effort. We
show that our framework can embed a watermark during the training of a deep
neural network from scratch, and during fine-tuning and distilling, without
impairing its performance. The embedded watermark does not disappear even after
fine-tuning or parameter pruning; the watermark remains complete even after 65%
of parameters are pruned.
Face Detection Using Improved Faster RCNN
Authors: Changzheng Zhang, Xiang Xu, Dandan Tu
Date: 2018-02-06 00:00:00
Tasks: Face Detection, Object, object-detection, Object Detection

Faster RCNN has achieved great success for generic object detection including
PASCAL object detection and MS COCO object detection. In this report, we
propose a detailed designed Faster RCNN method named FDNet1.0 for face
detection. Several techniques were employed including multi-scale training,
multi-scale testing, light-designed RCNN, some tricks for inference and a
vote-based ensemble method. Our method achieves two 1th places and one 2nd
place in three tasks over WIDER FACE validation dataset (easy set, medium set,
hard set).
Human and Machine Speaker Recognition Based on Short Trivial Events
Authors: Miao Zhang, Xiaofei Kang, Yanqing Wang, Lantian Li, Zhiyuan Tang, Haisheng Dai, Dong Wang
Date: 2017-11-15 00:00:00
Tasks: Speaker Recognition

Trivial events are ubiquitous in human to human conversations, e.g., cough,
laugh and sniff. Compared to regular speech, these trivial events are usually
short and unclear, thus generally regarded as not speaker discriminative and so
are largely ignored by present speaker recognition research. However, these
trivial events are highly valuable in some particular circumstances such as
forensic examination, as they are less subjected to intentional change, so can
be used to discover the genuine speaker from disguised speech. In this paper,
we collect a trivial event speech database that involves 75 speakers and 6
types of events, and report preliminary speaker recognition results on this
database, by both human listeners and machines. Particularly, the deep feature
learning technique recently proposed by our group is utilized to analyze and
recognize the trivial events, which leads to acceptable equal error rates
(EERs) despite the extremely short durations (0.2-0.5 seconds) of these events.
Comparing different types of events, 'hmm' seems more speaker discriminative.
Stacked Filters Stationary Flow For Hardware-Oriented Acceleration Of Deep Convolutional Neural Networks
Authors: Yuechao Gao, Nianhong Liu, Sheng Zhang
Date: 2018-01-23 00:00:00

To address memory and computation resource limitations for hardware-oriented
acceleration of deep convolutional neural networks (CNNs), we present a
computation flow, stacked filters stationary flow (SFS), and a corresponding
data encoding format, relative indexed compressed sparse filter format (CSF),
to make the best of data sparsity, and simplify data handling at execution
time. And we also propose a three dimensional Single Instruction Multiple Data
(3D-SIMD) processor architecture to illustrate how to accelerate deep CNNs by
taking advantage of SFS flow and CSF format. Comparing with the
state-of-the-art result (Han et al., 2016b), our methods achieve 1.11x
improvement in reducing the storage required by AlexNet, and 1.09x improvement
in reducing the storage required by SqueezeNet, without loss of accuracy on the
ImageNet dataset. Moreover, using these approaches, chip area for logics
handling irregular sparse data access can be saved. Comparing with the 2D-SIMD
processor structures in DVAS, ENVISION, etc., our methods achieve about 3.65x
processing element (PE) array utilization rate improvement (from 26.4\% to
96.5\%) on the data from Deep Compression on AlexNet.
Mining Public Opinion about Economic Issues: Twitter and the U.S. Presidential Election
Authors: Amir Karami, London S. Bennett, Xiaoyun He
Date: 2018-02-06 00:00:00
Tasks: Opinion Mining, Sentiment Analysis

Opinion polls have been the bridge between public opinion and politicians in
elections. However, developing surveys to disclose people's feedback with
respect to economic issues is limited, expensive, and time-consuming. In recent
years, social media such as Twitter has enabled people to share their opinions
regarding elections. Social media has provided a platform for collecting a
large amount of social media data. This paper proposes a computational public
opinion mining approach to explore the discussion of economic issues in social
media during an election. Current related studies use text mining methods
independently for election analysis and election prediction; this research
combines two text mining methods: sentiment analysis and topic modeling. The
proposed approach has effectively been deployed on millions of tweets to
analyze economic concerns of people during the 2012 US presidential election.
Goal Inference Improves Objective and Perceived Performance in Human-Robot Collaboration
Authors: Chang Liu, Jessica B. Hamrick, Jaime F. Fisac, Anca D. Dragan, J. Karl Hedrick, S. Shankar Sastry, Thomas L. Griffiths
Date: 2018-02-06 00:00:00
Tasks: Bayesian Inference, Task Planning

The study of human-robot interaction is fundamental to the design and use of
robotics in real-world applications. Robots will need to predict and adapt to
the actions of human collaborators in order to achieve good performance and
improve safety and end-user adoption. This paper evaluates a human-robot
collaboration scheme that combines the task allocation and motion levels of
reasoning: the robotic agent uses Bayesian inference to predict the next goal
of its human partner from his or her ongoing motion, and re-plans its own
actions in real time. This anticipative adaptation is desirable in many
practical scenarios, where humans are unable or unwilling to take on the
cognitive overhead required to explicitly communicate their intent to the
robot. A behavioral experiment indicates that the combination of goal inference
and dynamic task planning significantly improves both objective and perceived
performance of the human-robot team. Participants were highly sensitive to the
differences between robot behaviors, preferring to work with a robot that
adapted to their actions over one that did not.
Decomposition Methods with Deep Corrections for Reinforcement Learning
Authors: Maxime Bouton, Kyle Julian, Alireza Nakhaei, Kikuo Fujimura, Mykel J. Kochenderfer
Date: 2018-02-06 00:00:00
Tasks: Autonomous Driving, Decision Making, Management, Problem Decomposition, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Representation Learning, Sequential Decision Making

Decomposition methods have been proposed to approximate solutions to large
sequential decision making problems. In contexts where an agent interacts with
multiple entities, utility decomposition can be used to separate the global
objective into local tasks considering each individual entity independently. An
arbitrator is then responsible for combining the individual utilities and
selecting an action in real time to solve the global problem. Although these
techniques can perform well empirically, they rely on strong assumptions of
independence between the local tasks and sacrifice the optimality of the global
solution. This paper proposes an approach that improves upon such approximate
solutions by learning a correction term represented by a neural network. We
demonstrate this approach on a fisheries management problem where multiple
boats must coordinate to maximize their catch over time as well as on a
pedestrian avoidance problem for autonomous driving. In each problem,
decomposition methods can scale to multiple boats or pedestrians by using
strategies involving one entity. We verify empirically that the proposed
correction method significantly improves the decomposition method and
outperforms a policy trained on the full scale problem without utility
decomposition.
Scale-recurrent Network for Deep Image Deblurring
Authors: Xin Tao, Hongyun Gao, Yi Wang, Xiaoyong Shen, Jue Wang, Jiaya Jia
Date: 2018-02-06 00:00:00
Proceeding: CVPR 2018 6
Tasks: Deblurring, Image Deblurring, Image Relighting, Single Image Deblurring

In single image deblurring, the "coarse-to-fine" scheme, i.e. gradually
restoring the sharp image on different resolutions in a pyramid, is very
successful in both traditional optimization-based methods and recent
neural-network-based approaches. In this paper, we investigate this strategy
and propose a Scale-recurrent Network (SRN-DeblurNet) for this deblurring task.
Compared with the many recent learning-based approaches in [25], it has a
simpler network structure, a smaller number of parameters and is easier to
train. We evaluate our method on large-scale deblurring datasets with complex
motion. Results show that our method can produce better quality results than
state-of-the-arts, both quantitatively and qualitatively.
Question-Answer Selection in User to User Marketplace Conversations
Authors: Girish Kumar, Matthew Henderson, Shannon Chan, Hoang Nguyen, Lucas Ngoo
Date: 2018-02-06 00:00:00
Tasks: Answer Selection, Question Answering, Sentence

Sellers in user to user marketplaces can be inundated with questions from
potential buyers. Answers are often already available in the product
description. We collected a dataset of around 590K such questions and answers
from conversations in an online marketplace. We propose a question answering
system that selects a sentence from the product description using a
neural-network ranking model. We explore multiple encoding strategies, with
recurrent neural networks and feed-forward attention layers yielding good
results. This paper presents a demo to interactively pose buyer questions and
visualize the ranking scores of product description sentences from live online
listings.