pwc-archive/papers-with-abstracts huggingface dataset:
Transfer Learning with Binary Neural Networks
Authors: Sam Leroux, Steven Bohez, Tim Verbelen, Bert Vankeirsbilck, Pieter Simoens, Bart Dhoedt
Date: 2017-11-29 00:00:00
Tasks: Transfer Learning

Previous work has shown that it is possible to train deep neural networks
with low precision weights and activations. In the extreme case it is even
possible to constrain the network to binary values. The costly floating point
multiplications are then reduced to fast logical operations. High end smart
phones such as Google's Pixel 2 and Apple's iPhone X are already equipped with
specialised hardware for image processing and it is very likely that other
future consumer hardware will also have dedicated accelerators for deep neural
networks. Binary neural networks are attractive in this case because the
logical operations are very fast and efficient when implemented in hardware. We
propose a transfer learning based architecture where we first train a binary
network on Imagenet and then retrain part of the network for different tasks
while keeping most of the network fixed. The fixed binary part could be
implemented in a hardware accelerator while the last layers of the network are
evaluated in software. We show that a single binary neural network trained on
the Imagenet dataset can indeed be used as a feature extractor for other
datasets.
Representation Learning for Scale-free Networks
Authors: Rui Feng, Yang Yang, Wenjie Hu, Fei Wu, Yueting Zhuang
Date: 2017-11-29 00:00:00
Tasks: Link Prediction, Network Embedding, Representation Learning

Network embedding aims to learn the low-dimensional representations of
vertexes in a network, while structure and inherent properties of the network
is preserved. Existing network embedding works primarily focus on preserving
the microscopic structure, such as the first- and second-order proximity of
vertexes, while the macroscopic scale-free property is largely ignored.
Scale-free property depicts the fact that vertex degrees follow a heavy-tailed
distribution (i.e., only a few vertexes have high degrees) and is a critical
property of real-world networks, such as social networks. In this paper, we
study the problem of learning representations for scale-free networks. We first
theoretically analyze the difficulty of embedding and reconstructing a
scale-free network in the Euclidean space, by converting our problem to the
sphere packing problem. Then, we propose the "degree penalty" principle for
designing scale-free property preserving network embedding algorithm: punishing
the proximity between high-degree vertexes. We introduce two implementations of
our principle by utilizing the spectral techniques and a skip-gram model
respectively. Extensive experiments on six datasets show that our algorithms
are able to not only reconstruct heavy-tailed distributed degree distribution,
but also outperform state-of-the-art embedding models in various network mining
tasks, such as vertex classification and link prediction.
From source to target and back: symmetric bi-directional adaptive GAN
Authors: Paolo Russo, Fabio Maria Carlucci, Tatiana Tommasi, Barbara Caputo
Date: 2017-05-24 00:00:00
Proceeding: CVPR 2018 6
Tasks: Domain Adaptation, Image Generation, Unsupervised Domain Adaptation

The effectiveness of generative adversarial approaches in producing images
according to a specific style or visual domain has recently opened new
directions to solve the unsupervised domain adaptation problem. It has been
shown that source labeled images can be modified to mimic target samples making
it possible to train directly a classifier in the target domain, despite the
original lack of annotated data. Inverse mappings from the target to the source
domain have also been evaluated but only passing through adapted feature
spaces, thus without new image generation. In this paper we propose to better
exploit the potential of generative adversarial networks for adaptation by
introducing a novel symmetric mapping among domains. We jointly optimize
bi-directional image transformations combining them with target self-labeling.
Moreover we define a new class consistency loss that aligns the generators in
the two directions imposing to conserve the class identity of an image passing
through both domain mappings. A detailed qualitative and quantitative analysis
of the reconstructed images confirm the power of our approach. By integrating
the two domain specific classifiers obtained with our bi-directional network we
exceed previous state-of-the-art unsupervised adaptation results on four
different benchmark datasets.
Convolutional Neural Networks for Breast Cancer Screening: Transfer Learning with Exponential Decay
Authors: Hiba Chougrad, Hamid Zouaki, Omar Alheyane
Date: 2017-11-29 00:00:00
Tasks: Transfer Learning

In this paper, we propose a Computer Assisted Diagnosis (CAD) system based on
a deep Convolutional Neural Network (CNN) model, to build an end-to-end
learning process that classifies breast mass lesions. We investigate the impact
that has transfer learning when large data is scarce, and explore the proper
way to fine-tune the layers to learn features that are more specific to the new
data. The proposed approach showed better performance compared to other
proposals that classified the same dataset.
Scalable Object Detection for Stylized Objects
Authors: Aayush Garg, Thilo Will, William Darling, Willi Richert, Clemens Marschner
Date: 2017-11-27 00:00:00
Tasks: Logo Recognition, Object, object-detection, Object Detection, Retrieval

Following recent breakthroughs in convolutional neural networks and
monolithic model architectures, state-of-the-art object detection models can
reliably and accurately scale into the realm of up to thousands of classes.
Things quickly break down, however, when scaling into the tens of thousands,
or, eventually, to millions or billions of unique objects. Further, bounding
box-trained end-to-end models require extensive training data. Even though -
with some tricks using hierarchies - one can sometimes scale up to thousands of
classes, the labor requirements for clean image annotations quickly get out of
control. In this paper, we present a two-layer object detection method for
brand logos and other stylized objects for which prototypical images exist. It
can scale to large numbers of unique classes. Our first layer is a CNN from the
Single Shot Multibox Detector family of models that learns to propose regions
where some stylized object is likely to appear. The contents of a proposed
bounding box is then run against an image index that is targeted for the
retrieval task at hand. The proposed architecture scales to a large number of
object classes, allows to continously add new classes without retraining, and
exhibits state-of-the-art quality on a stylized object detection task such as
logo recognition.
RoboJam: A Musical Mixture Density Network for Collaborative Touchscreen Interaction
Authors: Charles P. Martin, Jim Torresen
Date: 2017-11-29 00:00:00
Tasks: BIG-bench Machine Learning

RoboJam is a machine-learning system for generating music that assists users
of a touchscreen music app by performing responses to their short
improvisations. This system uses a recurrent artificial neural network to
generate sequences of touchscreen interactions and absolute timings, rather
than high-level musical notes. To accomplish this, RoboJam's network uses a
mixture density layer to predict appropriate touch interaction locations in
space and time. In this paper, we describe the design and implementation of
RoboJam's network and how it has been integrated into a touchscreen music app.
A preliminary evaluation analyses the system in terms of training, musical
generation and user interaction.
Pipeline Generative Adversarial Networks for Facial Images Generation with Multiple Attributes
Authors: Ziqiang Zheng, Zhibin Yu, Haiyong Zheng, Chao Wang, Nan Wang
Date: 2017-11-29 00:00:00
Tasks: Attribute, Generative Adversarial Network, Image Generation

Generative Adversarial Networks are proved to be efficient on various kinds
of image generation tasks. However, it is still a challenge if we want to
generate images precisely. Many researchers focus on how to generate images
with one attribute. But image generation under multiple attributes is still a
tough work. In this paper, we try to generate a variety of face images under
multiple constraints using a pipeline process. The Pip-GAN (Pipeline Generative
Adversarial Network) we present employs a pipeline network structure which can
generate a complex facial image step by step using a neutral face image. We
applied our method on two face image databases and demonstrate its ability to
generate convincing novel images of unseen identities under multiple conditions
previously.
Unpaired Photo-to-Caricature Translation on Faces in the Wild
Authors: Ziqiang Zheng, Wang Chao, Zhibin Yu, Nan Wang, Haiyong Zheng, Bing Zheng
Date: 2017-11-29 00:00:00
Tasks: Caricature, Image-to-Image Translation, Photo-To-Caricature Translation, Representation Learning, Translation

Recently, image-to-image translation has been made much progress owing to the
success of conditional Generative Adversarial Networks (cGANs). And some
unpaired methods based on cycle consistency loss such as DualGAN, CycleGAN and
DiscoGAN are really popular. However, it's still very challenging for
translation tasks with the requirement of high-level visual information
conversion, such as photo-to-caricature translation that requires satire,
exaggeration, lifelikeness and artistry. We present an approach for learning to
translate faces in the wild from the source photo domain to the target
caricature domain with different styles, which can also be used for other
high-level image-to-image translation tasks. In order to capture global
structure with local statistics while translation, we design a dual pathway
model with one coarse discriminator and one fine discriminator. For generator,
we provide one extra perceptual loss in association with adversarial loss and
cycle consistency loss to achieve representation learning for two different
domains. Also the style can be learned by the auxiliary noise input.
Experiments on photo-to-caricature translation of faces in the wild show
considerable performance gain of our proposed method over state-of-the-art
translation methods as well as its potential real applications.
DS*: Tighter Lifting-Free Convex Relaxations for Quadratic Matching Problems
Authors: Florian Bernard, Christian Theobalt, Michael Moeller
Date: 2017-11-29 00:00:00
Proceeding: CVPR 2018 6
Tasks: Graph Matching

In this work we study convex relaxations of quadratic optimisation problems
over permutation matrices. While existing semidefinite programming approaches
can achieve remarkably tight relaxations, they have the strong disadvantage
that they lift the original $n {\times} n$-dimensional variable to an $n^2
{\times} n^2$-dimensional variable, which limits their practical applicability.
In contrast, here we present a lifting-free convex relaxation that is provably
at least as tight as existing (lifting-free) convex relaxations. We demonstrate
experimentally that our approach is superior to existing convex and non-convex
methods for various problems, including image arrangement and multi-graph
matching.
Unsupervised Part-based Weighting Aggregation of Deep Convolutional Features for Image Retrieval
Authors: Jian Xu, Cunzhao Shi, Chengzuo Qi, Chunheng Wang, Baihua Xiao
Date: 2017-05-03 00:00:00
Tasks: Image Retrieval, Retrieval

In this paper, we propose a simple but effective semantic part-based
weighting aggregation (PWA) for image retrieval. The proposed PWA utilizes the
discriminative filters of deep convolutional layers as part detectors.
Moreover, we propose the effective unsupervised strategy to select some part
detectors to generate the "probabilistic proposals", which highlight certain
discriminative parts of objects and suppress the noise of background. The final
global PWA representation could then be acquired by aggregating the regional
representations weighted by the selected "probabilistic proposals"
corresponding to various semantic content. We conduct comprehensive experiments
on four standard datasets and show that our unsupervised PWA outperforms the
state-of-the-art unsupervised and supervised aggregation methods. Code is
available at https://github.com/XJhaoren/PWA.
Cross-modal Recurrent Models for Weight Objective Prediction from Multimodal Time-series Data
Authors: Petar Veličković, Laurynas Karazija, Nicholas D. Lane, Sourav Bhattacharya, Edgar Liberis, Pietro Liò, Angela Chieh, Otmane Bellahsen, Matthieu Vegreville
Date: 2017-09-23 00:00:00
Tasks: Time Series, Time Series Analysis

We analyse multimodal time-series data corresponding to weight, sleep and
steps measurements. We focus on predicting whether a user will successfully
achieve his/her weight objective. For this, we design several deep long
short-term memory (LSTM) architectures, including a novel cross-modal LSTM
(X-LSTM), and demonstrate their superiority over baseline approaches. The
X-LSTM improves parameter efficiency by processing each modality separately and
allowing for information flow between them by way of recurrent
cross-connections. We present a general hyperparameter optimisation technique
for X-LSTMs, which allows us to significantly improve on the LSTM and a prior
state-of-the-art cross-modal approach, using a comparable number of parameters.
Finally, we visualise the model's predictions, revealing implications about
latent variables in this task.
SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving
Authors: Bichen Wu, Alvin Wan, Forrest Iandola, Peter H. Jin, Kurt Keutzer
Date: 2016-12-04 00:00:00
Tasks: Autonomous Driving, Object, object-detection, Object Detection, Real-Time Object Detection

Object detection is a crucial task for autonomous driving. In addition to requiring high accuracy to ensure safety, object detection for autonomous driving also requires real-time inference speed to guarantee prompt vehicle control, as well as small model size and energy efficiency to enable embedded system deployment. In this work, we propose SqueezeDet, a fully convolutional neural network for object detection that aims to simultaneously satisfy all of the above constraints. In our network, we use convolutional layers not only to extract feature maps but also as the output layer to compute bounding boxes and class probabilities. The detection pipeline of our model only contains a single forward pass of a neural network, thus it is extremely fast. Our model is fully-convolutional, which leads to a small model size and better energy efficiency. While achieving the same accuracy as previous baselines, our model is 30.4x smaller, 19.7x faster, and consumes 35.2x lower energy. The code is open-sourced at \url{https://github.com/BichenWuUCB/squeezeDet}.
Speaker-Sensitive Dual Memory Networks for Multi-Turn Slot Tagging
Authors: Young-Bum Kim, Sungjin Lee, Ruhi Sarikaya
Date: 2017-11-29 00:00:00
Tasks: Natural Language Understanding

In multi-turn dialogs, natural language understanding models can introduce
obvious errors by being blind to contextual information. To incorporate dialog
history, we present a neural architecture with Speaker-Sensitive Dual Memory
Networks which encode utterances differently depending on the speaker. This
addresses the different extents of information available to the system - the
system knows only the surface form of user utterances while it has the exact
semantics of system output. We performed experiments on real user data from
Microsoft Cortana, a commercial personal assistant. The result showed a
significant performance improvement over the state-of-the-art slot tagging
models using contextual information.
FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors
Authors: Yu Chen, Ying Tai, Xiaoming Liu, Chunhua Shen, Jian Yang
Date: 2017-11-29 00:00:00
Proceeding: CVPR 2018 6
Tasks: Decoder, Face Alignment, Generative Adversarial Network, Super-Resolution

Face Super-Resolution (SR) is a domain-specific super-resolution problem. The
specific facial prior knowledge could be leveraged for better super-resolving
face images. We present a novel deep end-to-end trainable Face Super-Resolution
Network (FSRNet), which makes full use of the geometry prior, i.e., facial
landmark heatmaps and parsing maps, to super-resolve very low-resolution (LR)
face images without well-aligned requirement. Specifically, we first construct
a coarse SR network to recover a coarse high-resolution (HR) image. Then, the
coarse HR image is sent to two branches: a fine SR encoder and a prior
information estimation network, which extracts the image features, and
estimates landmark heatmaps/parsing maps respectively. Both image features and
prior information are sent to a fine SR decoder to recover the HR image. To
further generate realistic faces, we propose the Face Super-Resolution
Generative Adversarial Network (FSRGAN) to incorporate the adversarial loss
into FSRNet. Moreover, we introduce two related tasks, face alignment and
parsing, as the new evaluation metrics for face SR, which address the
inconsistency of classic metrics w.r.t. visual perception. Extensive benchmark
experiments show that FSRNet and FSRGAN significantly outperforms state of the
arts for very LR face SR, both quantitatively and qualitatively. Code will be
made available upon publication.
Small Drone Field Experiment: Data Collection & Processing
Authors: Dalton Rosario, Christoph Borel, Damon Conover, Ryan McAlinden, Anthony Ortiz, Sarah Shiver, Blair Simon
Date: 2017-11-29 00:00:00
Tasks: 3D Reconstruction, Scene Understanding

Following an initiative formalized in April 2016 formally known as ARL West
between the U.S. Army Research Laboratory (ARL) and University of Southern
California's Institute for Creative Technologies (USC ICT), a field experiment
was coordinated and executed in the summer of 2016 by ARL, USC ICT, and
Headwall Photonics. The purpose was to image part of the USC main campus in Los
Angeles, USA, using two portable COTS (commercial off the shelf) aerial drone
solutions for data acquisition, for photogrammetry (3D reconstruction from
images), and fusion of hyperspectral data with the recovered set of 3D point
clouds representing the target area. The research aims for determining the
viability of having a machine capable of segmenting the target area into key
material classes (e.g., manmade structures, live vegetation, water) for use in
multiple purposes, to include providing the user with a more accurate scene
understanding and enabling the unsupervised automatic sampling of meaningful
material classes from the target area for adaptive semi-supervised machine
learning. In the latter, a target set library may be used for automatic machine
training with data of local material classes, as an example, to increase the
prediction chances of machines recognizing targets. The field experiment and
associated data post processing approach to correct for reflectance,
geo-rectify, recover the area's dense point clouds from images, register
spectral with elevation properties of scene surfaces from the independently
collected datasets, and generate the desired scene segmented maps are
discussed. Lessons learned from the experience are also highlighted throughout
the paper.
Facial Dynamics Interpreter Network: What are the Important Relations between Local Dynamics for Facial Trait Estimation?
Authors: Seong Tae Kim, Yong Man Ro
Date: 2017-11-29 00:00:00
Proceeding: ECCV 2018 9
Tasks: Age Estimation, Gender Classification, General Classification

Human face analysis is an important task in computer vision. According to
cognitive-psychological studies, facial dynamics could provide crucial cues for
face analysis. The motion of a facial local region in facial expression is
related to the motion of other facial local regions. In this paper, a novel
deep learning approach, named facial dynamics interpreter network, has been
proposed to interpret the important relations between local dynamics for
estimating facial traits from expression sequence. The facial dynamics
interpreter network is designed to be able to encode a relational importance,
which is used for interpreting the relation between facial local dynamics and
estimating facial traits. By comparative experiments, the effectiveness of the
proposed method has been verified. The important relations between facial local
dynamics are investigated by the proposed facial dynamics interpreter network
in gender classification and age estimation. Moreover, experimental results
show that the proposed method outperforms the state-of-the-art methods in
gender classification and age estimation.
Road Extraction by Deep Residual U-Net
Authors: Zhengxin Zhang, Qingjie Liu, Yunhong Wang
Date: 2017-11-29 00:00:00
Tasks: Lesion Segmentation, Lung Nodule Segmentation, Semantic Segmentation, Skin Cancer Segmentation

Road extraction from aerial images has been a hot research topic in the field
of remote sensing image analysis. In this letter, a semantic segmentation
neural network which combines the strengths of residual learning and U-Net is
proposed for road area extraction. The network is built with residual units and
has similar architecture to that of U-Net. The benefits of this model is
two-fold: first, residual units ease training of deep networks. Second, the
rich skip connections within the network could facilitate information
propagation, allowing us to design networks with fewer parameters however
better performance. We test our network on a public road dataset and compare it
with U-Net and other two state of the art deep learning based road extraction
methods. The proposed approach outperforms all the comparing methods, which
demonstrates its superiority over recently developed state of the arts.
Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption
Authors: Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Richard Nock, Giorgio Patrini, Guillaume Smith, Brian Thorne
Date: 2017-11-29 00:00:00
Tasks: Entity Resolution, Federated Learning, Privacy Preserving

Consider two data providers, each maintaining private records of different
feature sets about common entities. They aim to learn a linear model jointly in
a federated setting, namely, data is local and a shared model is trained from
locally computed updates. In contrast with most work on distributed learning,
in this scenario (i) data is split vertically, i.e. by features, (ii) only one
data provider knows the target variable and (iii) entities are not linked
across the data providers. Hence, to the challenge of private learning, we add
the potentially negative consequences of mistakes in entity resolution. Our
contribution is twofold. First, we describe a three-party end-to-end solution
in two phases ---privacy-preserving entity resolution and federated logistic
regression over messages encrypted with an additively homomorphic scheme---,
secure against a honest-but-curious adversary. The system allows learning
without either exposing data in the clear or sharing which entities the data
providers have in common. Our implementation is as accurate as a naive
non-private solution that brings all data in one place, and scales to problems
with millions of entities with hundreds of features. Second, we provide what is
to our knowledge the first formal analysis of the impact of entity resolution's
mistakes on learning, with results on how optimal classifiers, empirical
losses, margins and generalisation abilities are affected. Our results bring a
clear and strong support for federated learning: under reasonable assumptions
on the number and magnitude of entity resolution's mistakes, it can be
extremely beneficial to carry out federated learning in the setting where each
peer's data provides a significant uplift to the other.