pwc-archive/papers-with-abstracts huggingface dataset:
Multi-agent Path Finding with Continuous Time Viewed Through Satisfiability Modulo Theories (SMT)
Authors: Pavel Surynek
Date: 2019-03-23 00:00:00
Tasks: Multi-Agent Path Finding

This paper addresses a variant of multi-agent path finding (MAPF) in
continuous space and time. We present a new solving approach based on
satisfiability modulo theories (SMT) to obtain makespan optimal solutions. The
standard MAPF is a task of navigating agents in an undirected graph from given
starting vertices to given goal vertices so that agents do not collide with
each other in vertices of the graph. In the continuous version
(MAPF$^\mathcal{R}$) agents move in an $n$-dimensional Euclidean space along
straight lines that interconnect predefined positions. For simplicity, we work
with circular omni-directional agents having constant velocities in the 2D
plane. As agents can have different sizes and move smoothly along lines, a
non-colliding movement along certain lines with small agents can result in a
collision if the same movement is performed with larger agents. Our SMT-based
approach for MAPF$^\mathcal{R}$ called SMT-CBS$^\mathcal{R}$ reformulates the
Conflict-based Search (CBS) algorithm in terms of SMT concepts. We suggest lazy
generation of decision variables and constraints. Each time a new conflict is
discovered, the underlying encoding is extended with new variables and
constraints to eliminate the conflict. We compared SMT-CBS$^\mathcal{R}$ and
adaptations of CBS for the continuous variant of MAPF experimentally.
Feedback Network for Image Super-Resolution
Authors: Zhen Li, Jinglei Yang, Zheng Liu, Xiaomin Yang, Gwanggil Jeon, Wei Wu
Date: 2019-03-23 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Super-Resolution, Super-Resolution

Recent advances in image super-resolution (SR) explored the power of deep learning to achieve a better reconstruction performance. However, the feedback mechanism, which commonly exists in human visual system, has not been fully exploited in existing deep learning based image SR methods. In this paper, we propose an image super-resolution feedback network (SRFBN) to refine low-level representations with high-level information. Specifically, we use hidden states in an RNN with constraints to achieve such feedback manner. A feedback block is designed to handle the feedback connections and to generate powerful high-level representations. The proposed SRFBN comes with a strong early reconstruction ability and can create the final high-resolution image step by step. In addition, we introduce a curriculum learning strategy to make the network well suitable for more complicated tasks, where the low-resolution images are corrupted by multiple types of degradation. Extensive experimental results demonstrate the superiority of the proposed SRFBN in comparison with the state-of-the-art methods. Code is avaliable at https://github.com/Paper99/SRFBN_CVPR19.
Knowledge-Grounded Response Generation with Deep Attentional Latent-Variable Model
Authors: Hao-Tong Ye, Kai-Ling Lo, Shang-Yu Su, Yun-Nung Chen
Date: 2019-03-23 00:00:00
Tasks: Dialogue Generation, Response Generation

End-to-end dialogue generation has achieved promising results without using
handcrafted features and attributes specific for each task and corpus. However,
one of the fatal drawbacks in such approaches is that they are unable to
generate informative utterances, so it limits their usage from some real-world
conversational applications. This paper attempts at generating diverse and
informative responses with a variational generation model, which contains a
joint attention mechanism conditioning on the information from both dialogue
contexts and extra knowledge.
Semantic denoising autoencoders for retinal optical coherence tomography
Authors: Max-Heinrich Laves, Sontje Ihler, Lüder Alexander Kahrs, Tobias Ortmaier
Date: 2019-03-23 00:00:00
Tasks: Denoising, General Classification, Medical Diagnosis

Noise in speckle-prone optical coherence tomography tends to obfuscate
important details necessary for medical diagnosis. In this paper, a denoising
approach that preserves disease characteristics on retinal optical coherence
tomography images in ophthalmology is presented. By combining a deep
convolutional autoencoder with a priorly trained ResNet image classifier as
regularizer, the perceptibility of delicate details is encouraged and only
information-less background noise is filtered out. With our approach, higher
peak signal-to-noise ratios with $ \mathrm{PSNR} = 31.2\,\mathrm{dB} $ and
higher classification accuracy of $\mathrm{ACC} = 85.0\,\%$ can be achieved for
denoised images compared to state-of-the-art denoising with $ \mathrm{PSNR} =
29.4\,\mathrm{dB} $ or $\mathrm{ACC} = 70.3\,\%$, depending on the method. It
is shown that regularized autoencoders are capable of denoising retinal OCT
images without blurring details of diseases.
BitSplit-Net: Multi-bit Deep Neural Network with Bitwise Activation Function
Authors: Hyungjun Kim, Yulhwa Kim, Sungju Ryu, Jae-Joon Kim
Date: 2019-03-23 00:00:00
Tasks: GPU

Significant computational cost and memory requirements for deep neural
networks (DNNs) make it difficult to utilize DNNs in resource-constrained
environments. Binary neural network (BNN), which uses binary weights and binary
activations, has been gaining interests for its hardware-friendly
characteristics and minimal resource requirement. However, BNN usually suffers
from accuracy degradation. In this paper, we introduce "BitSplit-Net", a neural
network which maintains the hardware-friendly characteristics of BNN while
improving accuracy by using multi-bit precision. In BitSplit-Net, each bit of
multi-bit activations propagates independently throughout the network before
being merged at the end of the network. Thus, each bit path of the BitSplit-Net
resembles BNN and hardware friendly features of BNN, such as bitwise binary
activation function, are preserved in our scheme. We demonstrate that the
BitSplit version of LeNet-5, VGG-9, AlexNet, and ResNet-18 can be trained to
have similar classification accuracy at a lower computational cost compared to
conventional multi-bit networks with low bit precision (<= 4-bit). We further
evaluate BitSplit-Net on GPU with custom CUDA kernel, showing that BitSplit-Net
can achieve better hardware performance in comparison to conventional multi-bit
networks.
Coin.AI: A Proof-of-Useful-Work Scheme for Blockchain-based Distributed Deep Learning
Authors: Alejandro Baldominos, Yago Saez
Date: 2019-03-23 00:00:00

One decade ago, Bitcoin was introduced, becoming the first cryptocurrency and establishing the concept of "blockchain" as a distributed ledger. As of today, there are many different implementations of cryptocurrencies working over a blockchain, with different approaches and philosophies. However, many of them share one common feature: they require proof-of-work to support the generation of blocks (mining) and, eventually, the generation of money. This proof-of-work scheme often consists in the resolution of a cryptography problem, most commonly breaking a hash value, which can only be achieved through brute-force. The main drawback of proof-of-work is that it requires ridiculously large amounts of energy which do not have any useful outcome beyond supporting the currency. In this paper, we present a theoretical proposal that introduces a proof-of-useful-work scheme to support a cryptocurrency running over a blockchain, which we named Coin.AI. In this system, the mining scheme requires training deep learning models, and a block is only mined when the performance of such model exceeds a threshold. The distributed system allows for nodes to verify the models delivered by miners in an easy way (certainly much more efficiently than the mining process itself), determining when a block is to be generated. Additionally, this paper presents a proof-of-storage scheme for rewarding users that provide storage for the deep learning models, as well as a theoretical dissertation on how the mechanics of the system could be articulated with the ultimate goal of democratizing access to artificial intelligence.
Improving Adversarial Robustness via Guided Complement Entropy
Authors: Hao-Yun Chen, Jhao-Hong Liang, Shih-Chieh Chang, Jia-Yu Pan, Yu-Ting Chen, Wei Wei, Da-Cheng Juan
Date: 2019-03-23 00:00:00
Proceeding: ICCV 2019 10
Tasks: Adversarial Defense, Adversarial Robustness

Adversarial robustness has emerged as an important topic in deep learning as carefully crafted attack samples can significantly disturb the performance of a model. Many recent methods have proposed to improve adversarial robustness by utilizing adversarial training or model distillation, which adds additional procedures to model training. In this paper, we propose a new training paradigm called Guided Complement Entropy (GCE) that is capable of achieving "adversarial defense for free," which involves no additional procedures in the process of improving adversarial robustness. In addition to maximizing model probabilities on the ground-truth class like cross-entropy, we neutralize its probabilities on the incorrect classes along with a "guided" term to balance between these two terms. We show in the experiments that our method achieves better model robustness with even better performance compared to the commonly used cross-entropy training objective. We also show that our method can be used orthogonal to adversarial training across well-known methods with noticeable robustness gain. To the best of our knowledge, our approach is the first one that improves model robustness without compromising performance.
Spatially-weighted Anomaly Detection with Regression Model
Authors: Daiki Kimura, Minori Narita, Asim Munawar, Ryuki Tachibana
Date: 2019-03-23 00:00:00
Tasks: Anomaly Detection, model, regression

Visual anomaly detection is common in several applications including medical
screening and production quality check. Although a definition of the anomaly is
an unknown trend in data, in many cases some hints or samples of the anomaly
class can be given in advance. Conventional methods cannot use the available
anomaly data, and also do not have a robustness of noise. In this paper, we
propose a novel spatially-weighted reconstruction-loss-based anomaly detection
with a likelihood value from a regression model trained by all known data. The
spatial weights are calculated by a region of interest generated from employing
visualization of the regression model. We introduce some ways to combine with
various strategies to propose a state-of-the-art method. Comparing with other
methods on three different datasets, we empirically verify the proposed method
performs better than the others.
Data-driven Prognostics with Predictive Uncertainty Estimation using Ensemble of Deep Ordinal Regression Models
Authors: Vishnu TV, Diksha, Pankaj Malhotra, Lovekesh Vig, Gautam Shroff
Date: 2019-03-23 00:00:00
Tasks: regression, Time Series Analysis, Uncertainty Quantification

Prognostics or Remaining Useful Life (RUL) Estimation from multi-sensor time series data is useful to enable condition-based maintenance and ensure high operational availability of equipment. We propose a novel deep learning based approach for Prognostics with Uncertainty Quantification that is useful in scenarios where: (i) access to labeled failure data is scarce due to rarity of failures (ii) future operational conditions are unobserved and (iii) inherent noise is present in the sensor readings. All three scenarios mentioned are unavoidable sources of uncertainty in the RUL estimation process often resulting in unreliable RUL estimates. To address (i), we formulate RUL estimation as an Ordinal Regression (OR) problem, and propose LSTM-OR: deep Long Short Term Memory (LSTM) network based approach to learn the OR function. We show that LSTM-OR naturally allows for incorporation of censored operational instances in training along with the failed instances, leading to more robust learning. To address (ii), we propose a simple yet effective approach to quantify predictive uncertainty in the RUL estimation models by training an ensemble of LSTM-OR models. Through empirical evaluation on C-MAPSS turbofan engine benchmark datasets, we demonstrate that LSTM-OR is significantly better than the commonly used deep metric regression based approaches for RUL estimation, especially when failed training instances are scarce. Further, our uncertainty quantification approach yields high quality predictive uncertainty estimates while also leading to improved RUL estimates compared to single best LSTM-OR models.
Semi-Parametric Uncertainty Bounds for Binary Classification
Authors: Balázs Csanád Csáji, Ambrus Tamás
Date: 2019-03-23 00:00:00
Tasks: Binary Classification, Classification, General Classification, regression

The paper studies binary classification and aims at estimating the underlying
regression function which is the conditional expectation of the class labels
given the inputs. The regression function is the key component of the Bayes
optimal classifier, moreover, besides providing optimal predictions, it can
also assess the risk of misclassification. We aim at building non-asymptotic
confidence regions for the regression function and suggest three kernel-based
semi-parametric resampling methods. We prove that all of them guarantee regions
with exact coverage probabilities and they are strongly consistent.
An End-to-End Network for Generating Social Relationship Graphs
Authors: Arushi Goel, Keng Teck Ma, Cheston Tan
Date: 2019-03-23 00:00:00
Proceeding: CVPR 2019 6
Tasks: Attribute, Graph Generation, Visual Social Relationship Recognition

Socially-intelligent agents are of growing interest in artificial
intelligence. To this end, we need systems that can understand social
relationships in diverse social contexts. Inferring the social context in a
given visual scene not only involves recognizing objects, but also demands a
more in-depth understanding of the relationships and attributes of the people
involved. To achieve this, one computational approach for representing human
relationships and attributes is to use an explicit knowledge graph, which
allows for high-level reasoning. We introduce a novel end-to-end-trainable
neural network that is capable of generating a Social Relationship Graph - a
structured, unified representation of social relationships and attributes -
from a given input image. Our Social Relationship Graph Generation Network
(SRG-GN) is the first to use memory cells like Gated Recurrent Units (GRUs) to
iteratively update the social relationship states in a graph using scene and
attribute context. The neural network exploits the recurrent connections among
the GRUs to implement message passing between nodes and edges in the graph, and
results in significant improvement over previous methods for social
relationship recognition.
What Synthesis is Missing: Depth Adaptation Integrated with Weak Supervision for Indoor Scene Parsing
Authors: Keng-Chi Liu, Yi-Ting Shen, Jan P. Klopp, Liang-Gee Chen
Date: 2019-03-23 00:00:00
Proceeding: ICCV 2019 10
Tasks: Scene Parsing, Transfer Learning

Scene Parsing is a crucial step to enable autonomous systems to understand
and interact with their surroundings. Supervised deep learning methods have
made great progress in solving scene parsing problems, however, come at the
cost of laborious manual pixel-level annotation. To alleviate this effort
synthetic data as well as weak supervision have both been investigated.
Nonetheless, synthetically generated data still suffers from severe domain
shift while weak labels are often imprecise. Moreover, most existing works for
weakly supervised scene parsing are limited to salient foreground objects. The
aim of this work is hence twofold: Exploit synthetic data where feasible and
integrate weak supervision where necessary. More concretely, we address this
goal by utilizing depth as transfer domain because its synthetic-to-real
discrepancy is much lower than for color. At the same time, we perform weak
localization from easily obtainable image level labels and integrate both using
a novel contour-based scheme. Our approach is implemented as a teacher-student
learning framework to solve the transfer learning problem by generating a
pseudo ground truth. Using only depth-based adaptation, this approach already
outperforms previous transfer learning approaches on the popular indoor scene
parsing SUN RGB-D dataset. Our proposed two-stage integration more than halves
the gap towards fully supervised methods when compared to previous
state-of-the-art in transfer learning.
Auto-ReID: Searching for a Part-aware ConvNet for Person Re-Identification
Authors: Ruijie Quan, Xuanyi Dong, Yu Wu, Linchao Zhu, Yi Yang
Date: 2019-03-23 00:00:00
Proceeding: ICCV 2019 10
Tasks: Classification, General Classification, Neural Architecture Search, Person Re-Identification, Retrieval

Prevailing deep convolutional neural networks (CNNs) for person re-IDentification (reID) are usually built upon ResNet or VGG backbones, which were originally designed for classification. Because reID is different from classification, the architecture should be modified accordingly. We propose to automatically search for a CNN architecture that is specifically suitable for the reID task. There are three aspects to be tackled. First, body structural information plays an important role in reID but it is not encoded in backbones. Second, Neural Architecture Search (NAS) automates the process of architecture design without human effort, but no existing NAS methods incorporate the structure information of input images. Third, reID is essentially a retrieval task but current NAS algorithms are merely designed for classification. To solve these problems, we propose a retrieval-based search algorithm over a specifically designed reID search space, named Auto-ReID. Our Auto-ReID enables the automated approach to find an efficient and effective CNN architecture for reID. Extensive experiments demonstrate that the searched architecture achieves state-of-the-art performance while reducing 50% parameters and 53% FLOPs compared to others.
Progressive DNN Compression: A Key to Achieve Ultra-High Weight Pruning and Quantization Rates using ADMM
Authors: Shaokai Ye, Xiaoyu Feng, Tianyun Zhang, Xiaolong Ma, Sheng Lin, Zhengang Li, Kaidi Xu, Wujie Wen, Sijia Liu, Jian Tang, Makan Fardad, Xue Lin, Yongpan Liu, Yanzhi Wang
Date: 2019-03-23 00:00:00
Tasks: Model Compression, Quantization

Weight pruning and weight quantization are two important categories of DNN
model compression. Prior work on these techniques are mainly based on
heuristics. A recent work developed a systematic frame-work of DNN weight
pruning using the advanced optimization technique ADMM (Alternating Direction
Methods of Multipliers), achieving one of state-of-art in weight pruning
results. In this work, we first extend such one-shot ADMM-based framework to
guarantee solution feasibility and provide fast convergence rate, and
generalize to weight quantization as well. We have further developed a
multi-step, progressive DNN weight pruning and quantization framework, with
dual benefits of (i) achieving further weight pruning/quantization thanks to
the special property of ADMM regularization, and (ii) reducing the search space
within each step. Extensive experimental results demonstrate the superior
performance compared with prior work. Some highlights: (i) we achieve 246x,36x,
and 8x weight pruning on LeNet-5, AlexNet, and ResNet-50 models, respectively,
with (almost) zero accuracy loss; (ii) even a significant 61x weight pruning in
AlexNet (ImageNet) results in only minor degradation in actual accuracy
compared with prior work; (iii) we are among the first to derive notable weight
pruning results for ResNet and MobileNet models; (iv) we derive the first
lossless, fully binarized (for all layers) LeNet-5 for MNIST and VGG-16 for
CIFAR-10; and (v) we derive the first fully binarized (for all layers) ResNet
for ImageNet with reasonable accuracy loss.
Fast Underwater Image Enhancement for Improved Visual Perception
Authors: Md Jahidul Islam, Youya Xia, Junaed Sattar
Date: 2019-03-23 00:00:00
Tasks: Generative Adversarial Network, Image Enhancement, object-detection, Object Detection, Pose Estimation, Saliency Prediction, Underwater Image Restoration

In this paper, we present a conditional generative adversarial network-based model for real-time underwater image enhancement. To supervise the adversarial training, we formulate an objective function that evaluates the perceptual image quality based on its global content, color, local texture, and style information. We also present EUVP, a large-scale dataset of a paired and unpaired collection of underwater images (of `poor' and `good' quality) that are captured using seven different cameras over various visibility conditions during oceanic explorations and human-robot collaborative experiments. In addition, we perform several qualitative and quantitative evaluations which suggest that the proposed model can learn to enhance underwater image quality from both paired and unpaired training. More importantly, the enhanced images provide improved performances of standard models for underwater object detection, human pose estimation, and saliency prediction. These results validate that it is suitable for real-time preprocessing in the autonomy pipeline by visually-guided underwater robots. The model and associated training pipelines are available at https://github.com/xahidbuffon/funie-gan.
Scene Understanding for Autonomous Manipulation with Deep Learning
Authors: Anh Nguyen
Date: 2019-03-23 00:00:00
Tasks: Action Understanding, Affordance Detection, Caption Generation, Deep Learning, Image Segmentation, object-detection, Object Detection, Scene Understanding, Semantic Segmentation

Over the past few years, deep learning techniques have achieved tremendous
success in many visual understanding tasks such as object detection, image
segmentation, and caption generation. Despite this thriving in computer vision
and natural language processing, deep learning has not yet shown significant
impact in robotics. Due to the gap between theory and application, there are
many challenges when applying the results of deep learning to the real robotic
systems. In this study, our long-term goal is to bridge the gap between
computer vision and robotics by developing visual methods that can be used in
real robots. In particular, this work tackles two fundamental visual problems
for autonomous robotic manipulation: affordance detection and fine-grained
action understanding. Theoretically, we propose different deep architectures to
further improves the state of the art in each problem. Empirically, we show
that the outcomes of our proposed methods can be applied in real robots and
allow them to perform useful manipulation tasks.
Photorealistic Style Transfer via Wavelet Transforms
Authors: Jaejun Yoo, Youngjung Uh, Sanghyuk Chun, Byeongkyu Kang, Jung-Woo Ha
Date: 2019-03-23 00:00:00
Proceeding: ICCV 2019 10
Tasks: Style Transfer

Recent style transfer models have provided promising artistic results. However, given a photograph as a reference style, existing methods are limited by spatial distortions or unrealistic artifacts, which should not happen in real photographs. We introduce a theoretically sound correction to the network architecture that remarkably enhances photorealism and faithfully transfers the style. The key ingredient of our method is wavelet transforms that naturally fits in deep networks. We propose a wavelet corrected transfer based on whitening and coloring transforms (WCT$^2$) that allows features to preserve their structural information and statistical properties of VGG feature space during stylization. This is the first and the only end-to-end model that can stylize a $1024\times1024$ resolution image in 4.7 seconds, giving a pleasing and photorealistic quality without any post-processing. Last but not least, our model provides a stable video stylization without temporal constraints. Our code, generated images, and pre-trained models are all available at https://github.com/ClovaAI/WCT2.