pwc-archive/papers-with-abstracts huggingface dataset:
Hybrid Binary Networks: Optimizing for Accuracy, Efficiency and Memory
Authors: Ameya Prabhu, Vishal Batchu, Rohit Gajawada, Sri Aurobindo Munagala, Anoop Namboodiri
Date: 2018-04-11 00:00:00
Tasks: Binarization, Model Compression, Quantization

Binarization is an extreme network compression approach that provides large
computational speedups along with energy and memory savings, albeit at
significant accuracy costs. We investigate the question of where to binarize
inputs at layer-level granularity and show that selectively binarizing the
inputs to specific layers in the network could lead to significant improvements
in accuracy while preserving most of the advantages of binarization. We analyze
the binarization tradeoff using a metric that jointly models the input
binarization-error and computational cost and introduce an efficient algorithm
to select layers whose inputs are to be binarized. Practical guidelines based
on insights obtained from applying the algorithm to a variety of models are
discussed. Experiments on Imagenet dataset using AlexNet and ResNet-18 models
show 3-4% improvements in accuracy over fully binarized networks with minimal
impact on compression and computational speed. The improvements are even more
substantial on sketch datasets like TU-Berlin, where we match state-of-the-art
accuracy as well, getting over 8% increase in accuracies. We further show that
our approach can be applied in tandem with other forms of compression that deal
with individual layers or overall model compression (e.g., SqueezeNets). Unlike
previous quantization approaches, we are able to binarize the weights in the
last layers of a network, which often have a large number of parameters,
resulting in significant improvement in accuracy over fully binarized models.
MaskReID: A Mask Based Deep Ranking Neural Network for Person Re-identification
Authors: Lei Qi, Jing Huo, Lei Wang, Yinghuan Shi, Yang Gao
Date: 2018-04-11 00:00:00
Tasks: Image Retrieval, Person Re-Identification, Person Retrieval, Retrieval

Person retrieval faces many challenges including cluttered background,
appearance variations (e.g., illumination, pose, occlusion) among different
camera views and the similarity among different person's images. To address
these issues, we put forward a novel mask based deep ranking neural network
with a skipped fusing layer. Firstly, to alleviate the problem of cluttered
background, masked images with only the foreground regions are incorporated as
input in the proposed neural network. Secondly, to reduce the impact of the
appearance variations, the multi-layer fusion scheme is developed to obtain
more discriminative fine-grained information. Lastly, considering person
retrieval is a special image retrieval task, we propose a novel ranking loss to
optimize the whole network. The proposed ranking loss can further mitigate the
interference problem of similar negative samples when producing ranking
results. The extensive experiments validate the superiority of the proposed
method compared with the state-of-the-art methods on many benchmark datasets.
Generating Diverse and Accurate Visual Captions by Comparative Adversarial Learning
Authors: Dianqi Li, Qiuyuan Huang, Xiaodong He, Lei Zhang, Ming-Ting Sun
Date: 2018-04-03 00:00:00
Tasks: Diversity, Generative Adversarial Network

We study how to generate captions that are not only accurate in describing an
image but also discriminative across different images. The problem is both
fundamental and interesting, as most machine-generated captions, despite
phenomenal research progresses in the past several years, are expressed in a
very monotonic and featureless format. While such captions are normally
accurate, they often lack important characteristics in human languages -
distinctiveness for each caption and diversity for different images. To address
this problem, we propose a novel conditional generative adversarial network for
generating diverse captions across images. Instead of estimating the quality of
a caption solely on one image, the proposed comparative adversarial learning
framework better assesses the quality of captions by comparing a set of
captions within the image-caption joint space. By contrasting with
human-written captions and image-mismatched captions, the caption generator
effectively exploits the inherent characteristics of human languages, and
generates more discriminative captions. We show that our proposed network is
capable of producing accurate and diverse captions across images.
Real-world plant species identification based on deep convolutional neural networks and visual attention
Authors: Qingguo Xiao, Guangyao Li, Li Xie, Qiaochuan Chen
Date: 2018-04-11 00:00:00
Tasks: Data Augmentation

This paper investigates the issue of real-world identification to fulfill
better species protection. We focus on plant species identification as it is a
classic and hot issue. In tradition plant species identification the samples
are scanned specimen and the background is simple. However, real-world species
recognition is more challenging. We first systematically investigate what is
realistic species recognition and the difference from tradition plant species
recognition. To deal with the challenging task, an interdisciplinary
collaboration is presented based on the latest advances in computer science and
technology. We propose a novel framework and an effective data augmentation
method for deep learning in this paper. We first crop the image in terms of
visual attention before general recognition. Besides, we apply it as a data
augmentation method. We call the novel data augmentation approach attention
cropping (AC). Deep convolutional neural networks are trained to predict
species from a large amount of data. Extensive experiments on traditional
dataset and specific dataset for real-world recognition are conducted to
evaluate the performance of our approach. Experiments first demonstrate that
our approach achieves state-of-the-art results on different types of datasets.
Besides, we also evaluate the performance of data augmentation method AC.
Results show that AC provides superior performance. Compared with the precision
of methods without AC, the results with AC achieve substantial improvement.
Neural Vector Spaces for Unsupervised Information Retrieval
Authors: Christophe Van Gysel, Maarten de Rijke, Evangelos Kanoulas
Date: 2017-08-09 00:00:00
Tasks: Document Ranking, Feature Engineering, Information Retrieval, Model Selection, Retrieval, Specificity

We propose the Neural Vector Space Model (NVSM), a method that learns
representations of documents in an unsupervised manner for news article
retrieval. In the NVSM paradigm, we learn low-dimensional representations of
words and documents from scratch using gradient descent and rank documents
according to their similarity with query representations that are composed from
word representations. We show that NVSM performs better at document ranking
than existing latent semantic vector space methods. The addition of NVSM to a
mixture of lexical language models and a state-of-the-art baseline vector space
model yields a statistically significant increase in retrieval effectiveness.
Consequently, NVSM adds a complementary relevance signal. Next to semantic
matching, we find that NVSM performs well in cases where lexical matching is
needed.
  NVSM learns a notion of term specificity directly from the document
collection without feature engineering. We also show that NVSM learns
regularities related to Luhn significance. Finally, we give advice on how to
deploy NVSM in situations where model selection (e.g., cross-validation) is
infeasible. We find that an unsupervised ensemble of multiple models trained
with different hyperparameter values performs better than a single
cross-validated model. Therefore, NVSM can safely be used for ranking documents
without supervised relevance judgments.
Attentional Multilabel Learning over Graphs: A Message Passing Approach
Authors: Kien Do, Truyen Tran, Thin Nguyen, Svetha Venkatesh
Date: 2018-04-01 00:00:00
Tasks: General Classification, Graph Neural Network, Multi-Label Learning

We address a largely open problem of multilabel classification over graphs.
Unlike traditional vector input, a graph has rich variable-size substructures
which are related to the labels in some ways. We believe that uncovering these
relations might hold the key to classification performance and explainability.
We introduce GAML (Graph Attentional Multi-Label learning), a novel graph
neural network that can handle this problem effectively. GAML regards labels as
auxiliary nodes and models them in conjunction with the input graph. By
applying message passing and attention mechanisms to both the label nodes and
the input nodes iteratively, GAML can capture the relations between the labels
and the input subgraphs at various resolution scales. Moreover, our model can
take advantage of explicit label dependencies. It also scales linearly with the
number of labels and graph size thanks to our proposed hierarchical attention.
We evaluate GAML on an extensive set of experiments with both graph-structured
inputs and classical unstructured inputs. The results show that GAML
significantly outperforms other competing methods. Importantly, GAML enables
intuitive visualizations for better understanding of the label-substructure
relations and explanation of the model behaviors.
Generating Clues for Gender based Occupation De-biasing in Text
Authors: Nishtha Madaan, Gautam Singh, Sameep Mehta, Aditya Chetan, Brihi Joshi
Date: 2018-04-11 00:00:00

Vast availability of text data has enabled widespread training and use of AI
systems that not only learn and predict attributes from the text but also
generate text automatically. However, these AI models also learn gender, racial
and ethnic biases present in the training data. In this paper, we present the
first system that discovers the possibility that a given text portrays a gender
stereotype associated with an occupation. If the possibility exists, the system
offers counter-evidences of opposite gender also being associated with the same
occupation in the context of user-provided geography and timespan. The system
thus enables text de-biasing by assisting a human-in-the-loop. The system can
not only act as a text pre-processor before training any AI model but also help
human story writers write stories free of occupation-level gender bias in the
geographical and temporal context of their choice.
Unsupervised Segmentation of 3D Medical Images Based on Clustering and Deep Representation Learning
Authors: Takayasu Moriya, Holger R. Roth, Shota NAKAMURA, Hirohisa ODA, Kai Nagara, Masahiro Oda, Kensaku MORI
Date: 2018-04-11 00:00:00
Tasks: Clustering, Image Segmentation, Medical Image Segmentation, Representation Learning, Segmentation, Semantic Segmentation

This paper presents a novel unsupervised segmentation method for 3D medical
images. Convolutional neural networks (CNNs) have brought significant advances
in image segmentation. However, most of the recent methods rely on supervised
learning, which requires large amounts of manually annotated data. Thus, it is
challenging for these methods to cope with the growing amount of medical
images. This paper proposes a unified approach to unsupervised deep
representation learning and clustering for segmentation. Our proposed method
consists of two phases. In the first phase, we learn deep feature
representations of training patches from a target image using joint
unsupervised learning (JULE) that alternately clusters representations
generated by a CNN and updates the CNN parameters using cluster labels as
supervisory signals. We extend JULE to 3D medical images by utilizing 3D
convolutions throughout the CNN architecture. In the second phase, we apply
k-means to the deep representations from the trained CNN and then project
cluster labels to the target image in order to obtain the fully segmented
image. We evaluated our methods on three images of lung cancer specimens
scanned with micro-computed tomography (micro-CT). The automatic segmentation
of pathological regions in micro-CT could further contribute to the
pathological examination process. Hence, we aim to automatically divide each
image into the regions of invasive carcinoma, noninvasive carcinoma, and normal
tissue. Our experiments show the potential abilities of unsupervised deep
representation learning for medical image segmentation.
Unsupervised Pathology Image Segmentation Using Representation Learning with Spherical K-means
Authors: Takayasu Moriya, Holger R. Roth, Shota NAKAMURA, Hirohisa ODA, Kai Nagara, Masahiro Oda, Kensaku MORI
Date: 2018-04-11 00:00:00
Tasks: Clustering, Image Segmentation, Prognosis, Representation Learning, Segmentation, Semantic Segmentation

This paper presents a novel method for unsupervised segmentation of pathology
images. Staging of lung cancer is a major factor of prognosis. Measuring the
maximum dimensions of the invasive component in a pathology images is an
essential task. Therefore, image segmentation methods for visualizing the
extent of invasive and noninvasive components on pathology images could support
pathological examination. However, it is challenging for most of the recent
segmentation methods that rely on supervised learning to cope with unlabeled
pathology images. In this paper, we propose a unified approach to unsupervised
representation learning and clustering for pathology image segmentation. Our
method consists of two phases. In the first phase, we learn feature
representations of training patches from a target image using the spherical
k-means. The purpose of this phase is to obtain cluster centroids which could
be used as filters for feature extraction. In the second phase, we apply
conventional k-means to the representations extracted by the centroids and then
project cluster labels to the target images. We evaluated our methods on
pathology images of lung cancer specimen. Our experiments showed that the
proposed method outperforms traditional k-means segmentation and the
multithreshold Otsu method both quantitatively and qualitatively with an
improved normalized mutual information (NMI) score of 0.626 compared to 0.168
and 0.167, respectively. Furthermore, we found that the centroids can be
applied to the segmentation of other slices from the same sample.
OpenSeqSLAM2.0: An Open Source Toolbox for Visual Place Recognition Under Changing Conditions
Authors: Ben Talbot, Sourav Garg, Michael Milford
Date: 2018-04-06 00:00:00
Tasks: Visual Place Recognition

Visually recognising a traversed route - regardless of whether seen during
the day or night, in clear or inclement conditions, or in summer or winter - is
an important capability for navigating robots. Since SeqSLAM was introduced in
2012, a large body of work has followed exploring how robotic systems can use
the algorithm to meet the challenges posed by navigation in changing
environmental conditions. The following paper describes OpenSeqSLAM2.0, a fully
open source toolbox for visual place recognition under changing conditions.
Beyond the benefits of open access to the source code, OpenSeqSLAM2.0 provides
a number of tools to facilitate exploration of the visual place recognition
problem and interactive parameter tuning. Using the new open source platform,
it is shown for the first time how comprehensive parameter characterisations
provide new insights into many of the system components previously presented in
ad hoc ways and provide users with a guide to what system component options
should be used under what circumstances and why.
AFA-PredNet: The action modulation within predictive coding
Authors: Junpei Zhong, Angelo Cangelosi, Xinzheng Zhang, Tetsuya OGATA
Date: 2018-04-11 00:00:00
Tasks: Causal Inference, Prediction

The predictive processing (PP) hypothesizes that the predictive inference of
our sensorimotor system is encoded implicitly in the regularities between
perception and action. We propose a neural architecture in which such
regularities of active inference are encoded hierarchically. We further suggest
that this encoding emerges during the embodied learning process when the
appropriate action is selected to minimize the prediction error in perception.
Therefore, this predictive stream in the sensorimotor loop is generated in a
top-down manner. Specifically, it is constantly modulated by the motor actions
and is updated by the bottom-up prediction error signals. In this way, the
top-down prediction originally comes from the prior experience from both
perception and action representing the higher levels of this hierarchical
cognition. In our proposed embodied model, we extend the PredNet Network, a
hierarchical predictive coding network, with the motor action units implemented
by a multi-layer perceptron network (MLP) to modulate the network top-down
prediction. Two experiments, a minimalistic world experiment, and a mobile
robot experiment are conducted to evaluate the proposed model in a qualitative
way. In the neural representation, it can be observed that the causal inference
of predictive percept from motor actions can be also observed while the agent
is interacting with the environment.
MegDet: A Large Mini-Batch Object Detector
Authors: Chao Peng, Tete Xiao, Zeming Li, Yuning Jiang, Xiangyu Zhang, Kai Jia, Gang Yu, Jian Sun
Date: 2017-11-20 00:00:00
Proceeding: CVPR 2018 6
Tasks: GPU, Object, object-detection, Object Detection

The improvements in recent CNN-based object detection works, from R-CNN [11],
Fast/Faster R-CNN [10, 31] to recent Mask R-CNN [14] and RetinaNet [24], mainly
come from new network, new framework, or novel loss design. But mini-batch
size, a key factor in the training, has not been well studied. In this paper,
we propose a Large MiniBatch Object Detector (MegDet) to enable the training
with much larger mini-batch size than before (e.g. from 16 to 256), so that we
can effectively utilize multiple GPUs (up to 128 in our experiments) to
significantly shorten the training time. Technically, we suggest a learning
rate policy and Cross-GPU Batch Normalization, which together allow us to
successfully train a large mini-batch detector in much less time (e.g., from 33
hours to 4 hours), and achieve even better accuracy. The MegDet is the backbone
of our submission (mmAP 52.5%) to COCO 2017 Challenge, where we won the 1st
place of Detection task.
ExFuse: Enhancing Feature Fusion for Semantic Segmentation
Authors: Zhenli Zhang, Xiangyu Zhang, Chao Peng, Dazhi Cheng, Jian Sun
Date: 2018-04-11 00:00:00
Proceeding: ECCV 2018 9
Tasks: Segmentation, Semantic Segmentation

Modern semantic segmentation frameworks usually combine low-level and
high-level features from pre-trained backbone convolutional models to boost
performance. In this paper, we first point out that a simple fusion of
low-level and high-level features could be less effective because of the gap in
semantic levels and spatial resolution. We find that introducing semantic
information into low-level features and high-resolution details into high-level
features is more effective for the later fusion. Based on this observation, we
propose a new framework, named ExFuse, to bridge the gap between low-level and
high-level features thus significantly improve the segmentation quality by
4.0\% in total. Furthermore, we evaluate our approach on the challenging PASCAL
VOC 2012 segmentation benchmark and achieve 87.9\% mean IoU, which outperforms
the previous state-of-the-art results.
Learning Transferable Architectures for Scalable Image Recognition
Authors: Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le
Date: 2017-07-21 00:00:00
Proceeding: CVPR 2018 6
Tasks: Classification, image-classification, Image Classification, Neural Architecture Search

Developing neural network image classification models often requires
significant architecture engineering. In this paper, we study a method to learn
the model architectures directly on the dataset of interest. As this approach
is expensive when the dataset is large, we propose to search for an
architectural building block on a small dataset and then transfer the block to
a larger dataset. The key contribution of this work is the design of a new
search space (the "NASNet search space") which enables transferability. In our
experiments, we search for the best convolutional layer (or "cell") on the
CIFAR-10 dataset and then apply this cell to the ImageNet dataset by stacking
together more copies of this cell, each with their own parameters to design a
convolutional architecture, named "NASNet architecture". We also introduce a
new regularization technique called ScheduledDropPath that significantly
improves generalization in the NASNet models. On CIFAR-10 itself, NASNet
achieves 2.4% error rate, which is state-of-the-art. On ImageNet, NASNet
achieves, among the published works, state-of-the-art accuracy of 82.7% top-1
and 96.2% top-5 on ImageNet. Our model is 1.2% better in top-1 accuracy than
the best human-invented architectures while having 9 billion fewer FLOPS - a
reduction of 28% in computational demand from the previous state-of-the-art
model. When evaluated at different levels of computational cost, accuracies of
NASNets exceed those of the state-of-the-art human-designed models. For
instance, a small version of NASNet also achieves 74% top-1 accuracy, which is
3.1% better than equivalently-sized, state-of-the-art models for mobile
platforms. Finally, the learned features by NASNet used with the Faster-RCNN
framework surpass state-of-the-art by 4.0% achieving 43.1% mAP on the COCO
dataset.
Estimating Time-Varying Graphical Models
Authors: Jilei Yang, Jie Peng
Date: 2018-04-11 00:00:00
Tasks: Computational Efficiency

In this paper, we study time-varying graphical models based on data measured
over a temporal grid. Such models are motivated by the needs to describe and
understand evolving interacting relationships among a set of random variables
in many real applications, for instance the study of how stocks interact with
each other and how such interactions change over time.
  We propose a new model, LOcal Group Graphical Lasso Estimation (loggle),
under the assumption that the graph topology changes gradually over time.
Specifically, loggle uses a novel local group-lasso type penalty to efficiently
incorporate information from neighboring time points and to impose structural
smoothness of the graphs. We implement an ADMM based algorithm to fit the
loggle model. This algorithm utilizes blockwise fast computation and
pseudo-likelihood approximation to improve computational efficiency. An R
package loggle has also been developed.
  We evaluate the performance of loggle by simulation experiments. We also
apply loggle to S&P 500 stock price data and demonstrate that loggle is able to
reveal the interacting relationships among stocks and among industrial sectors
in a time period that covers the recent global financial crisis.
Demoiréing of Camera-Captured Screen Images Using Deep Convolutional Neural Network
Authors: Bolin Liu, Xiao Shu, Xiaolin Wu
Date: 2018-04-11 00:00:00

Taking photos of optoelectronic displays is a direct and spontaneous way of
transferring data and keeping records, which is widely practiced. However, due
to the analog signal interference between the pixel grids of the display screen
and camera sensor array, objectionable moir\'e (alias) patterns appear in
captured screen images. As the moir\'e patterns are structured and highly
variant, they are difficult to be completely removed without affecting the
underneath latent image. In this paper, we propose an approach of deep
convolutional neural network for demoir\'eing screen photos. The proposed DCNN
consists of a coarse-scale network and a fine-scale network. In the
coarse-scale network, the input image is first downsampled and then processed
by stacked residual blocks to remove the moir\'e artifacts. After that, the
fine-scale network upsamples the demoir\'ed low-resolution image back to the
original resolution. Extensive experimental results have demonstrated that the
proposed technique can efficiently remove the moir\'e patterns for camera
acquired screen images; the new technique outperforms the existing ones.