pwc-archive/papers-with-abstracts huggingface dataset:
Chan-Vese Reformulation for Selective Image Segmentation
Authors: Michael Roberts, Jack Spencer
Date: 2018-11-21 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Selective segmentation involves incorporating user input to partition an image into foreground and background, by discriminating between objects of a similar type. Typically, such methods involve introducing additional constraints to generic segmentation approaches. However, we show that this is often inconsistent with respect to common assumptions about the image. The proposed method introduces a new fitting term that is more useful in practice than the Chan-Vese framework. In particular, the idea is to define a term that allows for the background to consist of multiple regions of inhomogeneity. We provide comparitive experimental results to alternative approaches to demonstrate the advantages of the proposed method, broadening the possible application of these methods.
Gated Context Aggregation Network for Image Dehazing and Deraining
Authors: Dongdong Chen, Mingming He, Qingnan Fan, Jing Liao, Liheng Zhang, Dongdong Hou, Lu Yuan, Gang Hua
Date: 2018-11-21 00:00:00
Tasks: Image Dehazing, Rain Removal

Image dehazing aims to recover the uncorrupted content from a hazy image.
Instead of leveraging traditional low-level or handcrafted image priors as the
restoration constraints, e.g., dark channels and increased contrast, we propose
an end-to-end gated context aggregation network to directly restore the final
haze-free image. In this network, we adopt the latest smoothed dilation
technique to help remove the gridding artifacts caused by the widely-used
dilated convolution with negligible extra parameters, and leverage a gated
sub-network to fuse the features from different levels. Extensive experiments
demonstrate that our method can surpass previous state-of-the-art methods by a
large margin both quantitatively and qualitatively. In addition, to demonstrate
the generality of the proposed method, we further apply it to the image
deraining task, which also achieves the state-of-the-art performance. Code has
been made available at https://github.com/cddlyf/GCANet.
Semantic Stereo for Incidental Satellite Images
Authors: Marc Bosch, Kevin Foster, Gordon Christie, Sean Wang, Gregory D. Hager, Myron Brown
Date: 2018-11-21 00:00:00
Tasks: 3D Reconstruction, Scene Segmentation, Segmentation

The increasingly common use of incidental satellite images for stereo
reconstruction versus rigidly tasked binocular or trinocular coincident
collection is helping to enable timely global-scale 3D mapping; however,
reliable stereo correspondence from multi-date image pairs remains very
challenging due to seasonal appearance differences and scene change. Promising
recent work suggests that semantic scene segmentation can provide a robust
regularizing prior for resolving ambiguities in stereo correspondence and
reconstruction problems. To enable research for pairwise semantic stereo and
multi-view semantic 3D reconstruction with incidental satellite images, we have
established a large-scale public dataset including multi-view, multi-band
satellite images and ground truth geometric and semantic labels for two large
cities. To demonstrate the complementary nature of the stereo and segmentation
tasks, we present lightweight public baselines adapted from recent state of the
art convolutional neural network models and assess their performance.
SpotTune: Transfer Learning through Adaptive Fine-tuning
Authors: Yunhui Guo, Honghui Shi, Abhishek Kumar, Kristen Grauman, Tajana Rosing, Rogerio Feris
Date: 2018-11-21 00:00:00
Proceeding: CVPR 2019 6
Tasks: Inductive Bias, Transfer Learning

Transfer learning, which allows a source task to affect the inductive bias of
the target task, is widely used in computer vision. The typical way of
conducting transfer learning with deep neural networks is to fine-tune a model
pre-trained on the source task using data from the target task. In this paper,
we propose an adaptive fine-tuning approach, called SpotTune, which finds the
optimal fine-tuning strategy per instance for the target data. In SpotTune,
given an image from the target task, a policy network is used to make routing
decisions on whether to pass the image through the fine-tuned layers or the
pre-trained layers. We conduct extensive experiments to demonstrate the
effectiveness of the proposed approach. Our method outperforms the traditional
fine-tuning approach on 12 out of 14 standard datasets.We also compare SpotTune
with other state-of-the-art fine-tuning strategies, showing superior
performance. On the Visual Decathlon datasets, our method achieves the highest
score across the board without bells and whistles.
AttentionMask: Attentive, Efficient Object Proposal Generation Focusing on Small Objects
Authors: Christian Wilms, Simone Frintrop
Date: 2018-11-21 00:00:00
Tasks: Object Proposal Generation

We propose a novel approach for class-agnostic object proposal generation,
which is efficient and especially well-suited to detect small objects.
Efficiency is achieved by scale-specific objectness attention maps which focus
the processing on promising parts of the image and reduce the amount of sampled
windows strongly. This leads to a system, which is $33\%$ faster than the
state-of-the-art and clearly outperforming state-of-the-art in terms of average
recall. Secondly, we add a module for detecting small objects, which are often
missed by recent models. We show that this module improves the average recall
for small objects by about $53\%$.
Marginal Weighted Maximum Log-likelihood for Efficient Learning of Perturb-and-Map models
Authors: Tatiana Shpakova, Francis Bach, Anton Osokin
Date: 2018-11-21 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

We consider the structured-output prediction problem through probabilistic
approaches and generalize the "perturb-and-MAP" framework to more challenging
weighted Hamming losses, which are crucial in applications. While in principle
our approach is a straightforward marginalization, it requires solving many
related MAP inference problems. We show that for log-supermodular pairwise
models these operations can be performed efficiently using the machinery of
dynamic graph cuts. We also propose to use double stochastic gradient descent,
both on the data and on the perturbations, for efficient learning. Our
framework can naturally take weak supervision (e.g., partial labels) into
account. We conduct a set of experiments on medium-scale character recognition
and image segmentation, showing the benefits of our algorithms.
Sequential Neural Methods for Likelihood-free Inference
Authors: Conor Durkan, George Papamakarios, Iain Murray
Date: 2018-11-21 00:00:00

Likelihood-free inference refers to inference when a likelihood function
cannot be explicitly evaluated, which is often the case for models based on
simulators. Most of the literature is based on sample-based `Approximate
Bayesian Computation' methods, but recent work suggests that approaches based
on deep neural conditional density estimators can obtain state-of-the-art
results with fewer simulations. The neural approaches vary in how they choose
which simulations to run and what they learn: an approximate posterior or a
surrogate likelihood. This work provides some direct controlled comparisons
between these choices.
Inline Detection of Domain Generation Algorithms with Context-Sensitive Word Embeddings
Authors: Joewie J. Koh, Barton Rhodes
Date: 2018-11-21 00:00:00
Tasks: Word Embeddings

Domain generation algorithms (DGAs) are frequently employed by malware to
generate domains used for connecting to command-and-control (C2) servers.
Recent work in DGA detection leveraged deep learning architectures like
convolutional neural networks (CNNs) and character-level long short-term memory
networks (LSTMs) to classify domains. However, these classifiers perform poorly
with wordlist-based DGA families, which generate domains by pseudorandomly
concatenating dictionary words. We propose a novel approach that combines
context-sensitive word embeddings with a simple fully-connected classifier to
perform classification of domains based on word-level information. The word
embeddings were pre-trained on a large unrelated corpus and left frozen during
the training on domain data. The resulting small number of trainable parameters
enabled extremely short training durations, while the transfer of language
knowledge stored in the representations allowed for high-performing models with
small training datasets. We show that this architecture reliably outperformed
existing techniques on wordlist-based DGA families with just 30 DGA training
examples and achieved state-of-the-art performance with around 100 DGA training
examples, all while requiring an order of magnitude less time to train compared
to current techniques. Of special note is the technique's performance on the
matsnu DGA: the classifier attained a 89.5% detection rate with a 1:1,000 false
positive rate (FPR) after training on only 30 examples of the DGA domains, and
a 91.2% detection rate with a 1:10,000 FPR after 90 examples. Considering that
some of these DGAs have wordlists of several hundred words, our results
demonstrate that this technique does not rely on the classifier learning the
DGA wordlists. Instead, the classifier is able to learn the semantic signatures
of the wordlist-based DGA families.
Improving PSO Global Method for Feature Selection According to Iterations Global Search and Chaotic Theory
Authors: Shahin Pourbahrami
Date: 2018-11-21 00:00:00
Tasks: feature selection, General Classification

Making a simple model by choosing a limited number of features with the
purpose of reducing the computational complexity of the algorithms involved in
classification is one of the main issues in machine learning and data mining.
The aim of Feature Selection (FS) is to reduce the number of redundant and
irrelevant features and improve the accuracy of classification in a data set.
We propose an efficient ISPSO-GLOBAL (Improved Seeding Particle Swarm
Optimization GLOBAL) method which investigates the specified iterations to
produce prominent features and store them in storage list. The goal is to find
informative features based on its iteration frequency with favorable fitness
for the next generation and high exploration. Our method exploits of a new
initialization strategy in PSO which improves space search and utilizes chaos
theory to enhance the population initialization, then we offer a new formula to
determine the features size used in proposed method. Our experiments with
real-world data sets show that the performance of the ISPSO-GLOBAL is superior
comparing with state-of-the-art methods in most of the data sets.
Privacy-Preserving Collaborative Prediction using Random Forests
Authors: Irene Giacomelli, Somesh Jha, Ross Kleiman, David Page, Kyonghwan Yoon
Date: 2018-11-21 00:00:00
Tasks: Prediction, Privacy Preserving

We study the problem of privacy-preserving machine learning (PPML) for
ensemble methods, focusing our effort on random forests. In collaborative
analysis, PPML attempts to solve the conflict between the need for data sharing
and privacy. This is especially important in privacy sensitive applications
such as learning predictive models for clinical decision support from EHR data
from different clinics, where each clinic has a responsibility for its
patients' privacy. We propose a new approach for ensemble methods: each entity
learns a model, from its own data, and then when a client asks the prediction
for a new private instance, the answers from all the locally trained models are
used to compute the prediction in such a way that no extra information is
revealed. We implement this approach for random forests and we demonstrate its
high efficiency and potential accuracy benefit via experiments on real-world
datasets, including actual EHR data.
Surrogate-assisted parallel tempering for Bayesian neural learning
Authors: Rohitash Chandra, Konark Jain, Arpit Kapoor, Ashray Aman
Date: 2018-11-21 00:00:00
Tasks: Bayesian Inference, Decision Making, Uncertainty Quantification

Due to the need for robust uncertainty quantification, Bayesian neural learning has gained attention in the era of deep learning and big data. Markov Chain Monte-Carlo (MCMC) methods typically implement Bayesian inference which faces several challenges given a large number of parameters, complex and multimodal posterior distributions, and computational complexity of large neural network models. Parallel tempering MCMC addresses some of these limitations given that they can sample multimodal posterior distributions and utilize high-performance computing. However, certain challenges remain given large neural network models and big data. Surrogate-assisted optimization features the estimation of an objective function for models which are computationally expensive. In this paper, we address the inefficiency of parallel tempering MCMC for large-scale problems by combining parallel computing features with surrogate assisted likelihood estimation that describes the plausibility of a model parameter value, given specific observed data. Hence, we present surrogate-assisted parallel tempering for Bayesian neural learning for simple to computationally expensive models. Our results demonstrate that the methodology significantly lowers the computational cost while maintaining quality in decision making with Bayesian neural networks. The method has applications for a Bayesian inversion and uncertainty quantification for a broad range of numerical models.
Graph Refinement based Airway Extraction using Mean-Field Networks and Graph Neural Networks
Authors: Raghavendra Selvan, Thomas Kipf, Max Welling, Antonio Garcia-Uceda Juarez, Jesper H. Pedersen, Jens Petersen, Marleen de Bruijne
Date: 2018-11-21 00:00:00

Graph refinement, or the task of obtaining subgraphs of interest from over-complete graphs, can have many varied applications. In this work, we extract trees or collection of sub-trees from image data by, first deriving a graph-based representation of the volumetric data and then, posing the tree extraction as a graph refinement task. We present two methods to perform graph refinement. First, we use mean-field approximation (MFA) to approximate the posterior density over the subgraphs from which the optimal subgraph of interest can be estimated. Mean field networks (MFNs) are used for inference based on the interpretation that iterations of MFA can be seen as feed-forward operations in a neural network. This allows us to learn the model parameters using gradient descent. Second, we present a supervised learning approach using graph neural networks (GNNs) which can be seen as generalisations of MFNs. Subgraphs are obtained by training a GNN-based graph refinement model to directly predict edge probabilities. We discuss connections between the two classes of methods and compare them for the task of extracting airways from 3D, low-dose, chest CT data. We show that both the MFN and GNN models show significant improvement when compared to one baseline method, that is similar to a top performing method in the EXACT'09 Challenge, and a 3D U-Net based airway segmentation model, in detecting more branches with fewer false positives.
Computational Decomposition of Style for Controllable and Enhanced Style Transfer
Authors: Minchao Li, Shikui Tu, Lei Xu
Date: 2018-11-21 00:00:00
Tasks: Clustering, Style Transfer

Neural style transfer has been demonstrated to be powerful in creating
artistic image with help of Convolutional Neural Networks (CNN). However, there
is still lack of computational analysis of perceptual components of the
artistic style. Different from some early attempts which studied the style by
some pre-processing or post-processing techniques, we investigate the
characteristics of the style systematically based on feature map produced by
CNN. First, we computationally decompose the style into basic elements using
not only spectrum based methods including Fast Fourier Transform (FFT),
Discrete Cosine Transform (DCT) but also latent variable models such Principal
Component Analysis (PCA), Independent Component Analysis (ICA). Then, the
decomposition of style induces various ways of controlling the style elements
which could be embedded as modules in state-of-the-art style transfer
algorithms. Such decomposition of style brings several advantages. It enables
the computational coding of different artistic styles by our style basis with
similar styles clustering together, and thus it facilitates the mixing or
intervention of styles based on the style basis from more than one styles so
that compound style or new style could be generated to produce styled images.
Experiments demonstrate the effectiveness of our method on not only painting
style transfer but also sketch style transfer which indicates possible
applications on picture-to-sketch problems.
PersEmoN: A Deep Network for Joint Analysis of Apparent Personality, Emotion and Their Relationship
Authors: Le Zhang, Songyou Peng, Stefan Winkler
Date: 2018-11-21 00:00:00
Tasks: Emotion Recognition, Multi-Task Learning

Apparent personality and emotion analysis are both central to affective computing. Existing works solve them individually. In this paper we investigate if such high-level affect traits and their relationship can be jointly learned from face images in the wild. To this end, we introduce PersEmoN, an end-to-end trainable and deep Siamese-like network. It consists of two convolutional network branches, one for emotion and the other for apparent personality. Both networks share their bottom feature extraction module and are optimized within a multi-task learning framework. Emotion and personality networks are dedicated to their own annotated dataset. Furthermore, an adversarial-like loss function is employed to promote representation coherence among heterogeneous dataset sources. Based on this, we also explore the emotion-to-apparent-personality relationship. Extensive experiments demonstrate the effectiveness of PersEmoN.
A Fingerprint Indexing Method Based on Minutia Descriptor and Clustering
Authors: Gwang-Il Ri, Chol-Gyun Ri, Su-Rim Ji
Date: 2018-11-21 00:00:00
Tasks: Clustering

In this paper we propose a novel fingerprint indexing approach for speeding
up in the fingerprint recognition system. What kind of features are used for
indexing and how to employ the extracted features for searching are crucial for
the fingerprint indexing. In this paper, we select a minutia descriptor, which
has been used to improve the accuracy of the fingerprint matching, as a local
feature for indexing and construct a fixed-length feature vector which will be
used for searching from the minutia descriptors of the fingerprint image using
a clustering. And we propose a fingerprint searching approach that uses the
Euclidean distance between two feature vectors as the similarity between two
indexing features. Our indexing approach has several benefits. It reduces
searching time significantly and is irrespective of the existence of singular
points and robust even though the size of the fingerprint image is small or the
quality is low. And the constructed indexing vector by this approach is
independent of the features which are used for indexing based on the
geometrical relations between the minutiae, like one based on the minutiae
triplets. Thus, the proposed approach could be combined with other indexing
approaches to gain a better indexing performance.
Synetgy: Algorithm-hardware Co-design for ConvNet Accelerators on Embedded FPGAs
Authors: Yifan Yang, Qijing Huang, Bichen Wu, Tianjun Zhang, Liang Ma, Giulio Gambardella, Michaela Blott, Luciano Lavagno, Kees Vissers, John Wawrzynek, Kurt Keutzer
Date: 2018-11-21 00:00:00
Tasks: High-Level Synthesis

Using FPGAs to accelerate ConvNets has attracted significant attention in recent years. However, FPGA accelerator design has not leveraged the latest progress of ConvNets. As a result, the key application characteristics such as frames-per-second (FPS) are ignored in favor of simply counting GOPs, and results on accuracy, which is critical to application success, are often not even reported. In this work, we adopt an algorithm-hardware co-design approach to develop a ConvNet accelerator called Synetgy and a novel ConvNet model called DiracDeltaNet$^{\dagger}$. Both the accelerator and ConvNet are tailored to FPGA requirements. DiracDeltaNet, as the name suggests, is a ConvNet with only $1\times 1$ convolutions while spatial convolutions are replaced by more efficient shift operations. DiracDeltaNet achieves competitive accuracy on ImageNet (88.7\% top-5), but with 42$\times$ fewer parameters and 48$\times$ fewer OPs than VGG16. We further quantize DiracDeltaNet's weights to 4-bit and activations to 4-bits, with less than 1\% accuracy loss. These quantizations exploit well the nature of FPGA hardware. In short, DiracDeltaNet's small model size, low computational OP count, low precision and simplified operators allow us to co-design a highly customized computing unit for an FPGA. We implement the computing units for DiracDeltaNet on an Ultra96 SoC system through high-level synthesis. Our accelerator's final top-5 accuracy of 88.1\% on ImageNet, is higher than all the previously reported embedded FPGA accelerators. In addition, the accelerator reaches an inference speed of 66.3 FPS on the ImageNet classification task, surpassing prior works with similar accuracy by at least 11.6$\times$.
Compensated Integrated Gradients to Reliably Interpret EEG Classification
Authors: Kazuki Tachikawa, Yuji Kawai, Jihoon Park, Minoru Asada
Date: 2018-11-21 00:00:00
Tasks: Classification, EEG, Electroencephalogram (EEG), General Classification

Integrated gradients are widely employed to evaluate the contribution of
input features in classification models because it satisfies the axioms for
attribution of prediction. This method, however, requires an appropriate
baseline for reliable determination of the contributions. We propose a
compensated integrated gradients method that does not require a baseline. In
fact, the method compensates the attributions calculated by integrated
gradients at an arbitrary baseline using Shapley sampling. We prove that the
method retrieves reliable attributions if the processes of input features in a
classifier are mutually independent, and they are identical like shared weights
in convolutional neural networks. Using three electroencephalogram datasets, we
experimentally demonstrate that the attributions of the proposed method are
more reliable than those of the original integrated gradients, and its
computational complexity is much lower than that of Shapley sampling.
A Deep Tree-Structured Fusion Model for Single Image Deraining
Authors: Xueyang Fu, Qi Qi, Yue Huang, Xinghao Ding, Feng Wu, John Paisley
Date: 2018-11-21 00:00:00
Tasks: Rain Removal, Single Image Deraining

We propose a simple yet effective deep tree-structured fusion model based on
feature aggregation for the deraining problem. We argue that by effectively
aggregating features, a relatively simple network can still handle tough image
deraining problems well. First, to capture the spatial structure of rain we use
dilated convolutions as our basic network block. We then design a
tree-structured fusion architecture which is deployed within each block
(spatial information) and across all blocks (content information). Our method
is based on the assumption that adjacent features contain redundant
information. This redundancy obstructs generation of new representations and
can be reduced by hierarchically fusing adjacent features. Thus, the proposed
model is more compact and can effectively use spatial and content information.
Experiments on synthetic and real-world datasets show that our network achieves
better deraining results with fewer parameters.