pwc-archive/papers-with-abstracts huggingface dataset:
SMPLR: Deep SMPL reverse for 3D human pose and shape recovery
Authors: Meysam Madadi, Hugo Bertiche, Sergio Escalera
Date: 2018-12-27 00:00:00
Tasks: Decoder, Denoising

Current state-of-the-art in 3D human pose and shape recovery relies on deep neural networks and statistical morphable body models, such as the Skinned Multi-Person Linear model (SMPL). However, regardless of the advantages of having both body pose and shape, SMPL-based solutions have shown difficulties to predict 3D bodies accurately. This is mainly due to the unconstrained nature of SMPL, which may generate unrealistic body meshes. Because of this, regression of SMPL parameters is a difficult task, often addressed with complex regularization terms. In this paper we propose to embed SMPL within a deep model to accurately estimate 3D pose and shape from a still RGB image. We use CNN-based 3D joint predictions as an intermediate representation to regress SMPL pose and shape parameters. Later, 3D joints are reconstructed again in the SMPL output. This module can be seen as an autoencoder where the encoder is a deep neural network and the decoder is SMPL model. We refer to this as SMPL reverse (SMPLR). By implementing SMPLR as an encoder-decoder we avoid the need of complex constraints on pose and shape. Furthermore, given that in-the-wild datasets usually lack accurate 3D annotations, it is desirable to lift 2D joints to 3D without pairing 3D annotations with RGB images. Therefore, we also propose a denoising autoencoder (DAE) module between CNN and SMPLR, able to lift 2D joints to 3D and partially recover from structured error. We evaluate our method on SURREAL and Human3.6M datasets, showing improvement over SMPL-based state-of-the-art alternatives by about 4 and 25 millimeters, respectively.
Improving Generalization of Deep Neural Networks by Leveraging Margin Distribution
Authors: Shen-Huan Lyu, Lu Wang, Zhi-Hua Zhou
Date: 2018-12-27 00:00:00
Proceeding: ICLR 2019 5
Tasks: Representation Learning

Recent research has used margin theory to analyze the generalization performance for deep neural networks (DNNs). The existed results are almost based on the spectrally-normalized minimum margin. However, optimizing the minimum margin ignores a mass of information about the entire margin distribution, which is crucial to generalization performance. In this paper, we prove a generalization upper bound dominated by the statistics of the entire margin distribution. Compared with the minimum margin bounds, our bound highlights an important measure for controlling the complexity, which is the ratio of the margin standard deviation to the expected margin. We utilize a convex margin distribution loss function on the deep neural networks to validate our theoretical results by optimizing the margin ratio. Experiments and visualizations confirm the effectiveness of our approach and the correlation between generalization gap and margin ratio.
Advancing the State of the Art in Open Domain Dialog Systems through the Alexa Prize
Authors: Chandra Khatri, Behnam Hedayatnia, Anu Venkatesh, Jeff Nunn, Yi Pan, Qing Liu, Han Song, Anna Gottardi, Sanjeev Kwatra, Sanju Pancholi, Ming Cheng, Qinglang Chen, Lauren Stubel, Karthik Gopalakrishnan, Kate Bland, Raefer Gabriel, Arindam Mandal, Dilek Hakkani-Tur, Gene Hwang, Nate Michel, Eric King, Rohit Prasad
Date: 2018-12-27 00:00:00
Tasks: Knowledge Graphs, Management, Natural Language Understanding, Open-Domain Dialog, speech-recognition, Speech Recognition

Building open domain conversational systems that allow users to have engaging
conversations on topics of their choice is a challenging task. Alexa Prize was
launched in 2016 to tackle the problem of achieving natural, sustained,
coherent and engaging open-domain dialogs. In the second iteration of the
competition in 2018, university teams advanced the state of the art by using
context in dialog models, leveraging knowledge graphs for language
understanding, handling complex utterances, building statistical and
hierarchical dialog managers, and leveraging model-driven signals from user
responses. The 2018 competition also included the provision of a suite of tools
and models to the competitors including the CoBot (conversational bot) toolkit,
topic and dialog act detection models, conversation evaluators, and a sensitive
content detection model so that the competing teams could focus on building
knowledge-rich, coherent and engaging multi-turn dialog systems. This paper
outlines the advances developed by the university teams as well as the Alexa
Prize team to achieve the common goal of advancing the science of
Conversational AI. We address several key open-ended problems such as
conversational speech recognition, open domain natural language understanding,
commonsense reasoning, statistical dialog management, and dialog evaluation.
These collaborative efforts have driven improved experiences by Alexa users to
an average rating of 3.61, the median duration of 2 mins 18 seconds, and
average turns to 14.6, increases of 14%, 92%, 54% respectively since the launch
of the 2018 competition. For conversational speech recognition, we have
improved our relative Word Error Rate by 55% and our relative Entity Error Rate
by 34% since the launch of the Alexa Prize. Socialbots improved in quality
significantly more rapidly in 2018, in part due to the release of the CoBot
toolkit.
Off-the-grid model based deep learning (O-MODL)
Authors: Aniket Pramanik, Hemant Kumar Aggarwal, Mathews Jacob
Date: 2018-12-27 00:00:00
Tasks: Deep Learning, Image Reconstruction

We introduce a model based off-the-grid image reconstruction algorithm using
deep learned priors. The main difference of the proposed scheme with current
deep learning strategies is the learning of non-linear annihilation relations
in Fourier space. We rely on a model based framework, which allows us to use a
significantly smaller deep network, compared to direct approaches that also
learn how to invert the forward model. Preliminary comparisons against image
domain MoDL approach demonstrates the potential of the off-the-grid
formulation. The main benefit of the proposed scheme compared to structured
low-rank methods is the quite significant reduction in computational
complexity.
Finite State Machines for Semantic Scene Parsing and Segmentation
Authors: Hichem Sahbi
Date: 2018-12-27 00:00:00
Tasks: Object, Scene Parsing, Segmentation

We introduce in this work a novel stochastic inference process, for scene
annotation and object class segmentation, based on finite state machines
(FSMs). The design principle of our framework is generative and based on
building, for a given scene, finite state machines that encode annotation
lattices, and inference consists in finding and scoring the best configurations
in these lattices. Different novel operations are defined using our FSM
framework including reordering, segmentation, visual transduction, and label
dependency modeling. All these operations are combined together in order to
achieve annotation as well as object class segmentation.
CAN: Constrained Attention Networks for Multi-Aspect Sentiment Analysis
Authors: Mengting Hu, Shiwan Zhao, Li Zhang, Keke Cai, Zhong Su, Renhong Cheng, Xiaowei Shen
Date: 2018-12-27 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: General Classification, Sentence, Sentiment Analysis, Sentiment Classification

Aspect level sentiment classification is a fine-grained sentiment analysis task. To detect the sentiment towards a particular aspect in a sentence, previous studies have developed various attention-based methods for generating aspect-specific sentence representations. However, the attention may inherently introduce noise and downgrade the performance. In this paper, we propose constrained attention networks (CAN), a simple yet effective solution, to regularize the attention for multi-aspect sentiment analysis, which alleviates the drawback of the attention mechanism. Specifically, we introduce orthogonal regularization on multiple aspects and sparse regularization on each single aspect. Experimental results on two public datasets demonstrate the effectiveness of our approach. We further extend our approach to multi-task settings and outperform the state-of-the-art methods.
Neuromemrisitive Architecture of HTM with On-Device Learning and Neurogenesis
Authors: Abdullah M. Zyarah, Dhireesha Kudithipudi
Date: 2018-12-27 00:00:00

Hierarchical temporal memory (HTM) is a biomimetic sequence memory algorithm
that holds promise for invariant representations of spatial and spatiotemporal
inputs. This paper presents a comprehensive neuromemristive crossbar
architecture for the spatial pooler (SP) and the sparse distributed
representation classifier, which are fundamental to the algorithm. There are
several unique features in the proposed architecture that tightly link with the
HTM algorithm. A memristor that is suitable for emulating the HTM synapses is
identified and a new Z-window function is proposed. The architecture exploits
the concept of synthetic synapses to enable potential synapses in the HTM. The
crossbar for the SP avoids dark spots caused by unutilized crossbar regions and
supports rapid on-chip training within 2 clock cycles. This research also
leverages plasticity mechanisms such as neurogenesis and homeostatic intrinsic
plasticity to strengthen the robustness and performance of the SP. The proposed
design is benchmarked for image recognition tasks using MNIST and Yale faces
datasets, and is evaluated using different metrics including entropy,
sparseness, and noise robustness. Detailed power analysis at different stages
of the SP operations is performed to demonstrate the suitability for mobile
platforms.
QRFA: A Data-Driven Model of Information-Seeking Dialogues
Authors: Svitlana Vakulenko, Kate Revoredo, Claudio Di Ciccio, Maarten de Rijke
Date: 2018-12-27 00:00:00

Understanding the structure of interaction processes helps us to improve
information-seeking dialogue systems. Analyzing an interaction process boils
down to discovering patterns in sequences of alternating utterances exchanged
between a user and an agent. Process mining techniques have been successfully
applied to analyze structured event logs, discovering the underlying process
models or evaluating whether the observed behavior is in conformance with the
known process. In this paper, we apply process mining techniques to discover
patterns in conversational transcripts and extract a new model of
information-seeking dialogues, QRFA, for Query, Request, Feedback, Answer. Our
results are grounded in an empirical evaluation across multiple conversational
datasets from different domains, which was never attempted before. We show that
the QRFA model better reflects conversation flows observed in real
information-seeking conversations than models proposed previously. Moreover,
QRFA allows us to identify malfunctioning in dialogue system transcripts as
deviations from the expected conversation flow described by the model via
conformance analysis.
S4-Net: Geometry-Consistent Semi-Supervised Semantic Segmentation
Authors: Sinisa Stekovic, Friedrich Fraundorfer, Vincent Lepetit
Date: 2018-12-27 00:00:00
Tasks: Segmentation, Semantic Segmentation, Semi-Supervised Semantic Segmentation

We show that it is possible to learn semantic segmentation from very limited
amounts of manual annotations, by enforcing geometric 3D constraints between
multiple views. More exactly, image locations corresponding to the same
physical 3D point should all have the same label. We show that introducing such
constraints during learning is very effective, even when no manual label is
available for a 3D point, and can be done simply by employing techniques from
'general' semi-supervised learning to the context of semantic segmentation. To
demonstrate this idea, we use RGB-D image sequences of rigid scenes, for a
4-class segmentation problem derived from the ScanNet dataset. Starting from
RGB-D sequences with a few annotated frames, we show that we can incorporate
RGB-D sequences without any manual annotations to improve the performance,
which makes our approach very convenient. Furthermore, we demonstrate our
approach for semantic segmentation of objects on the LabelFusion dataset, where
we show that one manually labeled image in a scene is sufficient for high
performance on the whole scene.
Surface Networks via General Covers
Authors: Niv Haim, Nimrod Segol, Heli Ben-Hamu, Haggai Maron, Yaron Lipman
Date: 2018-12-27 00:00:00
Proceeding: ICCV 2019 10
Tasks: Retrieval

Developing deep learning techniques for geometric data is an active and fruitful research area. This paper tackles the problem of sphere-type surface learning by developing a novel surface-to-image representation. Using this representation we are able to quickly adapt successful CNN models to the surface setting. The surface-image representation is based on a covering map from the image domain to the surface. Namely, the map wraps around the surface several times, making sure that every part of the surface is well represented in the image. Differently from previous surface-to-image representations, we provide a low distortion coverage of all surface parts in a single image. Specifically, for the use case of learning spherical signals, our representation provides a low distortion alternative to several popular spherical parameterizations used in deep learning. We have used the surface-to-image representation to apply standard CNN architectures to 3D models as well as spherical signals. We show that our method achieves state of the art or comparable results on the tasks of shape retrieval, shape classification and semantic shape segmentation.
No-Reference Color Image Quality Assessment: From Entropy to Perceptual Quality
Authors: Xiaoqiao Chen, Qingyi Zhang, Manhui Lin, Guangyi Yang, Chu He
Date: 2018-12-27 00:00:00
Tasks: Image Quality Assessment, No-Reference Image Quality Assessment

This paper presents a high-performance general-purpose no-reference (NR)
image quality assessment (IQA) method based on image entropy. The image
features are extracted from two domains. In the spatial domain, the mutual
information between the color channels and the two-dimensional entropy are
calculated. In the frequency domain, the two-dimensional entropy and the mutual
information of the filtered sub-band images are computed as the feature set of
the input color image. Then, with all the extracted features, the support
vector classifier (SVC) for distortion classification and support vector
regression (SVR) are utilized for the quality prediction, to obtain the final
quality assessment score. The proposed method, which we call entropy-based
no-reference image quality assessment (ENIQA), can assess the quality of
different categories of distorted images, and has a low complexity. The
proposed ENIQA method was assessed on the LIVE and TID2013 databases and showed
a superior performance. The experimental results confirmed that the proposed
ENIQA method has a high consistency of objective and subjective assessment on
color images, which indicates the good overall performance and generalization
ability of ENIQA. The source code is available on github
https://github.com/jacob6/ENIQA.
Robustness to Out-of-Distribution Inputs via Task-Aware Generative Uncertainty
Authors: Rowan McAllister, Gregory Kahn, Jeff Clune, Sergey Levine
Date: 2018-12-27 00:00:00

Deep learning provides a powerful tool for machine perception when the
observations resemble the training data. However, real-world robotic systems
must react intelligently to their observations even in unexpected
circumstances. This requires a system to reason about its own uncertainty given
unfamiliar, out-of-distribution observations. Approximate Bayesian approaches
are commonly used to estimate uncertainty for neural network predictions, but
can struggle with out-of-distribution observations. Generative models can in
principle detect out-of-distribution observations as those with a low estimated
density. However, the mere presence of an out-of-distribution input does not by
itself indicate an unsafe situation. In this paper, we present a method for
uncertainty-aware robotic perception that combines generative modeling and
model uncertainty to cope with uncertainty stemming from out-of-distribution
states. Our method estimates an uncertainty measure about the model's
prediction, taking into account an explicit (generative) model of the
observation distribution to handle out-of-distribution inputs. This is
accomplished by probabilistically projecting observations onto the training
distribution, such that out-of-distribution inputs map to uncertain
in-distribution observations, which in turn produce uncertain task-related
predictions, but only if task-relevant parts of the image change. We evaluate
our method on an action-conditioned collision prediction task with both
simulated and real data, and demonstrate that our method of projecting
out-of-distribution observations improves the performance of four standard
Bayesian and non-Bayesian neural network approaches, offering more favorable
trade-offs between the proportion of time a robot can remain autonomous and the
proportion of impending crashes successfully avoided.
Neural Architecture Search Over a Graph Search Space
Authors: Stanisław Jastrzębski, Quentin de Laroussilhe, Mingxing Tan, Xiao Ma, Neil Houlsby, Andrea Gesmundo
Date: 2018-12-27 00:00:00
Tasks: image-classification, Image Classification, Neural Architecture Search

Neural Architecture Search (NAS) enabled the discovery of state-of-the-art architectures in many domains. However, the success of NAS depends on the definition of the search space. Current search spaces are defined as a static sequence of decisions and a set of available actions for each decision. Each possible sequence of actions defines an architecture. We propose a more expressive class of search space: directed graphs. In our formalism, each decision is a vertex and each action is an edge. This allows us to model iterative and branching architecture design decisions. We demonstrate in simulation, and on image classification experiments, basic iterative and branching search structures, and show that the graph representation improves sample efficiency.
Low Latency Privacy Preserving Inference
Authors: Alon Brutzkus, Oren Elisha, Ran Gilad-Bachrach
Date: 2018-12-27 00:00:00
Proceeding: ICLR 2019 5
Tasks: Privacy Preserving, Transfer Learning

When applying machine learning to sensitive data, one has to find a balance between accuracy, information security, and computational-complexity. Recent studies combined Homomorphic Encryption with neural networks to make inferences while protecting against information leakage. However, these methods are limited by the width and depth of neural networks that can be used (and hence the accuracy) and exhibit high latency even for relatively simple networks. In this study we provide two solutions that address these limitations. In the first solution, we present more than $10\times$ improvement in latency and enable inference on wider networks compared to prior attempts with the same level of security. The improved performance is achieved by novel methods to represent the data during the computation. In the second solution, we apply the method of transfer learning to provide private inference services using deep networks with latency of $\sim0.16$ seconds. We demonstrate the efficacy of our methods on several computer vision tasks.
Sampling Using Neural Networks for colorizing the grayscale images
Authors: Wonbong Jang
Date: 2018-12-27 00:00:00
Tasks: Colorization

The main idea of this paper is to explore the possibilities of generating
samples from the neural networks, mostly focusing on the colorization of the
grey-scale images. I will compare the existing methods for colorization and
explore the possibilities of using new generative modeling to the task of
colorization. The contributions of this paper are to compare the existing
structures with similar generating structures(Decoders) and to apply the novel
structures including Conditional VAE(CVAE), Conditional Wasserstein GAN with
Gradient Penalty(CWGAN-GP), CWGAN-GP with L1 reconstruction loss, Adversarial
Generative Encoders(AGE) and Introspective VAE(IVAE). I trained these models
using CIFAR-10 images. To measure the performance, I use Inception Score(IS)
which measures how distinctive each image is and how diverse overall samples
are as well as human eyes for CIFAR-10 images. It turns out that CVAE with L1
reconstruction loss and IVAE achieve the highest score in IS. CWGAN-GP with L1
tends to learn faster than CWGAN-GP, but IS does not increase from CWGAN-GP.
CWGAN-GP tends to generate more diverse images than other models using
reconstruction loss. Also, I figured out that the proper regularization plays a
vital role in generative modeling.
Sparse Nonnegative CANDECOMP/PARAFAC Decomposition in Block Coordinate Descent Framework: A Comparison Study
Authors: Deqing Wang, Feng-Yu Cong, Tapani Ristaniemi
Date: 2018-12-27 00:00:00
Tasks: Tensor Decomposition

Nonnegative CANDECOMP/PARAFAC (NCP) decomposition is an important tool to
process nonnegative tensor. Sometimes, additional sparse regularization is
needed to extract meaningful nonnegative and sparse components. Thus, an
optimization method for NCP that can impose sparsity efficiently is required.
In this paper, we construct NCP with sparse regularization (sparse NCP) by
l1-norm. Several popular optimization methods in block coordinate descent
framework are employed to solve the sparse NCP, all of which are deeply
analyzed with mathematical solutions. We compare these methods by experiments
on synthetic and real tensor data, both of which contain third-order and
fourth-order cases. After comparison, the methods that have fast computation
and high effectiveness to impose sparsity will be concluded. In addition, we
proposed an accelerated method to compute the objective function and relative
error of sparse NCP, which has significantly improved the computation of tensor
decomposition especially for higher-order tensor.
Chart-Text: A Fully Automated Chart Image Descriptor
Authors: Abhijit Balaji, Thuvaarakkesh Ramanathan, Venkateshwarlu Sonathi
Date: 2018-12-27 00:00:00

Images greatly help in understanding, interpreting and visualizing data.
Adding textual description to images is the first and foremost principle of web
accessibility. Visually impaired users using screen readers will use these
textual descriptions to get better understanding of images present in digital
contents. In this paper, we propose Chart-Text a novel fully automated system
that creates textual description of chart images. Given a PNG image of a chart,
our Chart-Text system creates a complete textual description of it. First, the
system classifies the type of chart and then it detects and classifies the
labels and texts in the charts. Finally, it uses specific image processing
algorithms to extract relevant information from the chart images. Our proposed
system achieves an accuracy of 99.72% in classifying the charts and an accuracy
of 78.9% in extracting the data and creating the corresponding textual
description.