pwc-archive/papers-with-abstracts huggingface dataset:
Terrain RL Simulator
Authors: Glen Berseth, Xue Bin Peng, Michiel Van de Panne
Date: 2018-04-17 00:00:00

We provide $89$ challenging simulation environments that range in difficulty.
The difficulty of solving a task is linked not only to the number of dimensions
in the action space but also to the size and shape of the distribution of
configurations the agent experiences. Therefore, we are releasing a number of
simulation environments that include randomly generated terrain. The library
also provides simple mechanisms to create new environments with different agent
morphologies and the option to modify the distribution of generated terrain. We
believe using these and other more complex simulations will help push the field
closer to creating human-level intelligence.
Deep Object Co-Segmentation
Authors: Weihao Li, Omid Hosseini jafari, Carsten Rother
Date: 2018-04-17 00:00:00
Tasks: Decoder, Object, Segmentation

This work presents a deep object co-segmentation (DOCS) approach for segmenting common objects of the same class within a pair of images. This means that the method learns to ignore common, or uncommon, background stuff and focuses on objects. If multiple object classes are presented in the image pair, they are jointly extracted as foreground. To address this task, we propose a CNN-based Siamese encoder-decoder architecture. The encoder extracts high-level semantic features of the foreground objects, a mutual correlation layer detects the common objects, and finally, the decoder generates the output foreground masks for each image. To train our model, we compile a large object co-segmentation dataset consisting of image pairs from the PASCAL VOC dataset with common objects masks. We evaluate our approach on commonly used datasets for co-segmentation tasks and observe that our approach consistently outperforms competing methods, for both seen and unseen object classes.
Improving Lexical Choice in Neural Machine Translation
Authors: Toan Q. Nguyen, David Chiang
Date: 2017-10-03 00:00:00
Proceeding: NAACL 2018 6
Tasks: Machine Translation, Translation, Word Embeddings

We explore two solutions to the problem of mistranslating rare words in
neural machine translation. First, we argue that the standard output layer,
which computes the inner product of a vector representing the context with all
possible output word embeddings, rewards frequent words disproportionately, and
we propose to fix the norms of both vectors to a constant value. Second, we
integrate a simple lexical module which is jointly trained with the rest of the
model. We evaluate our approaches on eight language pairs with data sizes
ranging from 100k to 8M words, and achieve improvements of up to +4.3 BLEU,
surpassing phrase-based translation in nearly all settings.
Graph-based Selective Outlier Ensembles
Authors: Hamed Sarvari, Carlotta Domeniconi, Giovanni Stilo
Date: 2018-04-17 00:00:00
Tasks: outlier ensembles

An ensemble technique is characterized by the mechanism that generates the
components and by the mechanism that combines them. A common way to achieve the
consensus is to enable each component to equally participate in the aggregation
process. A problem with this approach is that poor components are likely to
negatively affect the quality of the consensus result. To address this issue,
alternatives have been explored in the literature to build selective classifier
and cluster ensembles, where only a subset of the components contributes to the
computation of the consensus. Of the family of ensemble methods, outlier
ensembles are the least studied. Only recently, the selection problem for
outlier ensembles has been discussed. In this work we define a new graph-based
class of ranking selection methods. A method in this class is characterized by
two main steps: (1) Mapping the rankings onto a graph structure; and (2) Mining
the resulting graph to identify a subset of rankings. We define a specific
instance of the graph-based ranking selection class. Specifically, we map the
problem of selecting ensemble components onto a mining problem in a graph. An
extensive evaluation was conducted on a variety of heterogeneous data and
methods. Our empirical results show that our approach outperforms
state-of-the-art selective outlier ensemble techniques.
Im2Avatar: Colorful 3D Reconstruction from a Single Image
Authors: Yongbin Sun, Ziwei Liu, Yue Wang, Sanjay E. Sarma
Date: 2018-04-17 00:00:00
Tasks: 3D Reconstruction, Hallucination

Existing works on single-image 3D reconstruction mainly focus on shape
recovery. In this work, we study a new problem, that is, simultaneously
recovering 3D shape and surface color from a single image, namely "colorful 3D
reconstruction". This problem is both challenging and intriguing because the
ability to infer textured 3D model from a single image is at the core of visual
understanding. Here, we propose an end-to-end trainable framework, Colorful
Voxel Network (CVN), to tackle this problem. Conditioned on a single 2D input,
CVN learns to decompose shape and surface color information of a 3D object into
a 3D shape branch and a surface color branch, respectively. Specifically, for
the shape recovery, we generate a shape volume with the state of its voxels
indicating occupancy. For the surface color recovery, we combine the strength
of appearance hallucination and geometric projection by concurrently learning a
regressed color volume and a 2D-to-3D flow volume, which are then fused into a
blended color volume. The final textured 3D model is obtained by sampling color
from the blended color volume at the positions of occupied voxels in the shape
volume. To handle the severe sparse volume representations, a novel loss
function, Mean Squared False Cross-Entropy Loss (MSFCEL), is designed.
Extensive experiments demonstrate that our approach achieves significant
improvement over baselines, and shows great generalization across diverse
object categories and arbitrary viewpoints.
DGPose: Deep Generative Models for Human Body Analysis
Authors: Rodrigo de Bem, Arnab Ghosh, Thalaiyasingam Ajanthan, Ondrej Miksik, Adnane Boukhayma, N. Siddharth, Philip Torr
Date: 2018-04-17 00:00:00
Tasks: Disentanglement, Pose Estimation, Pose Transfer

Deep generative modelling for human body analysis is an emerging problem with many interesting applications. However, the latent space learned by such approaches is typically not interpretable, resulting in less flexibility. In this work, we present deep generative models for human body analysis in which the body pose and the visual appearance are disentangled. Such a disentanglement allows independent manipulation of pose and appearance, and hence enables applications such as pose-transfer without specific training for such a task. Our proposed models, the Conditional-DGPose and the Semi-DGPose, have different characteristics. In the first, body pose labels are taken as conditioners, from a fully-supervised training set. In the second, our structured semi-supervised approach allows for pose estimation to be performed by the model itself and relaxes the need for labelled data. Therefore, the Semi-DGPose aims for the joint understanding and generation of people in images. It is not only capable of mapping images to interpretable latent representations but also able to map these representations back to the image space. We compare our models with relevant baselines, the ClothNet-Body and the Pose Guided Person Generation networks, demonstrating their merits on the Human3.6M, ChictopiaPlus and DeepFashion benchmarks.
Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
Authors: Veronika Cheplygina, Marleen de Bruijne, Josien P. W. Pluim
Date: 2018-04-17 00:00:00
Tasks: BIG-bench Machine Learning, Medical Image Analysis, Transfer Learning

Machine learning (ML) algorithms have made a tremendous impact in the field
of medical imaging. While medical imaging datasets have been growing in size, a
challenge for supervised ML algorithms that is frequently mentioned is the lack
of annotated data. As a result, various methods which can learn with less/other
types of supervision, have been proposed. We review semi-supervised, multiple
instance, and transfer learning in medical imaging, both in diagnosis/detection
or segmentation tasks. We also discuss connections between these learning
scenarios, and opportunities for future research.
Similarity between Learning Outcomes from Course Objectives using Semantic Analysis, Blooms taxonomy and Corpus statistics
Authors: Atish Pawar, Vijay Mago
Date: 2018-04-17 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity

The course description provided by instructors is an essential piece of
information as it defines what is expected from the instructor and what he/she
is going to deliver during a particular course. One of the key components of a
course description is the Learning Objectives section. The contents of this
section are used by program managers who are tasked to compare and match two
different courses during the development of Transfer Agreements between various
institutions. This research introduces the development of semantic similarity
algorithms to calculate the similarity between two learning objectives of the
same domain. We present a novel methodology which deals with the semantic
similarity by using a previously established algorithm and integrating it with
the domain corpus utilizing domain statistics. The disambiguated domain serves
as a supervised learning data for the algorithm. We also introduce Bloom Index
to calculate the similarity between action verbs in the Learning Objectives
referring to the Blooms taxonomy.
Training a Binary Weight Object Detector by Knowledge Transfer for Autonomous Driving
Authors: Jiaolong Xu, Peng Wang, Heng Yang, Antonio M. López
Date: 2018-04-17 00:00:00
Tasks: Autonomous Driving, object-detection, Object Detection, Transfer Learning

Autonomous driving has harsh requirements of small model size and energy efficiency, in order to enable the embedded system to achieve real-time on-board object detection. Recent deep convolutional neural network based object detectors have achieved state-of-the-art accuracy. However, such models are trained with numerous parameters and their high computational costs and large storage prohibit the deployment to memory and computation resource limited systems. Low-precision neural networks are popular techniques for reducing the computation requirements and memory footprint. Among them, binary weight neural network (BWN) is the extreme case which quantizes the float-point into just $1$ bit. BWNs are difficult to train and suffer from accuracy deprecation due to the extreme low-bit representation. To address this problem, we propose a knowledge transfer (KT) method to aid the training of BWN using a full-precision teacher network. We built DarkNet- and MobileNet-based binary weight YOLO-v2 detectors and conduct experiments on KITTI benchmark for car, pedestrian and cyclist detection. The experimental results show that the proposed method maintains high detection accuracy while reducing the model size of DarkNet-YOLO from 257 MB to 8.8 MB and MobileNet-YOLO from 193 MB to 7.9 MB.
Simplifying the minimax disparity model for determining OWA weights in large-scale problems
Authors: Thuy Hong Nguyen
Date: 2018-04-17 00:00:00
Tasks: Decision Making

In the context of multicriteria decision making, the ordered weighted
averaging (OWA) functions play a crucial role in aggregating multiple criteria
evaluations into an overall assessment supporting the decision makers' choice.
Determining OWA weights, therefore, is an essential part of this process.
Available methods for determining OWA weights, however, often require heavy
computational loads in real-life large-scale optimization problems. In this
paper, we propose a new approach to simplify the well-known minimax disparity
model for determining OWA weights. For this purpose, we use to the binomial
decomposition framework in which natural constraints can be imposed on the
level of complexity of the weight distribution. The original problem of
determining OWA weights is thereby transformed into a smaller scale
optimization problem, formulated in terms of the coefficients in the binomial
decomposition. Our preliminary results show that a small set of these
coefficients can encode for an appropriate full-dimensional set of OWA weights.
Classifying Antimicrobial and Multifunctional Peptides with Bayesian Network Models
Authors: Rainier Barrett, Shaoyi Jiang, Andrew D. White
Date: 2018-04-17 00:00:00

Bayesian network models are finding success in characterizing
enzyme-catalyzed reactions, slow conformational changes, predicting enzyme
inhibition, and genomics. In this work, we apply them to statistical modeling
of peptides by simultaneously identifying amino acid sequence motifs and using
a motif-based model to clarify the role motifs may play in antimicrobial
activity. We construct models of increasing sophistication, demonstrating how
chemical knowledge of a peptide system may be embedded without requiring new
derivation of model fitting equations after changing model structure. These
models are used to construct classifiers with good performance (94% accuracy,
Matthews correlation coefficient of 0.87) at predicting antimicrobial activity
in peptides, while at the same time being built of interpretable parameters. We
demonstrate use of these models to identify peptides that are potentially both
antimicrobial and antifouling, and show that the background distribution of
amino acids could play a greater role in activity than sequence motifs do. This
provides an advancement in the type of peptide activity modeling that can be
done and the ease in which models can be constructed.
Learning Awareness Models
Authors: Brandon Amos, Laurent Dinh, Serkan Cabi, Thomas Rothörl, Sergio Gómez Colmenarejo, Alistair Muldal, Tom Erez, Yuval Tassa, Nando de Freitas, Misha Denil
Date: 2018-04-17 00:00:00
Proceeding: ICLR 2018 1

We consider the setting of an agent with a fixed body interacting with an
unknown and uncertain external world. We show that models trained to predict
proprioceptive information about the agent's body come to represent objects in
the external world. In spite of being trained with only internally available
signals, these dynamic body models come to represent external objects through
the necessity of predicting their effects on the agent's own body. That is, the
model learns holistic persistent representations of objects in the world, even
though the only training signals are body signals. Our dynamics model is able
to successfully predict distributions over 132 sensor readings over 100 steps
into the future and we demonstrate that even when the body is no longer in
contact with an object, the latent variables of the dynamics model continue to
represent its shape. We show that active data collection by maximizing the
entropy of predictions about the body---touch sensors, proprioception and
vestibular information---leads to learning of dynamic models that show superior
performance when used for control. We also collect data from a real robotic
hand and show that the same models can be used to answer questions about
properties of objects in the real world. Videos with qualitative results of our
models are available at https://goo.gl/mZuqAV.
Can clustering scale sublinearly with its clusters? A variational EM acceleration of GMMs and $k$-means
Authors: Dennis Forster, Jörg Lücke
Date: 2017-11-09 00:00:00
Tasks: Clustering

One iteration of standard $k$-means (i.e., Lloyd's algorithm) or standard EM
for Gaussian mixture models (GMMs) scales linearly with the number of clusters
$C$, data points $N$, and data dimensionality $D$. In this study, we explore
whether one iteration of $k$-means or EM for GMMs can scale sublinearly with
$C$ at run-time, while improving the clustering objective remains effective.
The tool we apply for complexity reduction is variational EM, which is
typically used to make training of generative models with exponentially many
hidden states tractable. Here, we apply novel theoretical results on truncated
variational EM to make tractable clustering algorithms more efficient. The
basic idea is to use a partial variational E-step which reduces the linear
complexity of $\mathcal{O}(NCD)$ required for a full E-step to a sublinear
complexity. Our main observation is that the linear dependency on $C$ can be
reduced to a dependency on a much smaller parameter $G$ which relates to
cluster neighborhood relations. We focus on two versions of partial variational
EM for clustering: variational GMM, scaling with $\mathcal{O}(NG^2D)$, and
variational $k$-means, scaling with $\mathcal{O}(NGD)$ per iteration. Empirical
results show that these algorithms still require comparable numbers of
iterations to improve the clustering objective to same values as $k$-means. For
data with many clusters, we consequently observe reductions of net
computational demands between two and three orders of magnitude. More
generally, our results provide substantial empirical evidence in favor of
clustering to scale sublinearly with $C$.
Weakly Supervised Object Discovery by Generative Adversarial & Ranking Networks
Authors: Ali Diba, Vivek Sharma, Rainer Stiefelhagen, Luc van Gool
Date: 2017-11-22 00:00:00
Tasks: Decoder, Object, object-detection, Object Detection, Object Discovery, Weakly Supervised Object Detection

The deep generative adversarial networks (GAN) recently have been shown to be
promising for different computer vision applications, like image edit- ing,
synthesizing high resolution images, generating videos, etc. These networks and
the corresponding learning scheme can handle various visual space map- pings.
We approach GANs with a novel training method and learning objective, to
discover multiple object instances for three cases: 1) synthesizing a picture
of a specific object within a cluttered scene; 2) localizing different
categories in images for weakly supervised object detection; and 3) improving
object discov- ery in object detection pipelines. A crucial advantage of our
method is that it learns a new deep similarity metric, to distinguish multiple
objects in one im- age. We demonstrate that the network can act as an
encoder-decoder generating parts of an image which contain an object, or as a
modified deep CNN to rep- resent images for object detection in supervised and
weakly supervised scheme. Our ranking GAN offers a novel way to search through
images for object specific patterns. We have conducted experiments for
different scenarios and demonstrate the method performance for object
synthesizing and weakly supervised object detection and classification using
the MS-COCO and PASCAL VOC datasets.
Three-Dimensional GPU-Accelerated Active Contours for Automated Localization of Cells in Large Images
Authors: Mahsa Lotfollahi, Sebastian Berisha, Leila Saadatifard, Laura Montier, Jokubas Ziburkus, David Mayerich
Date: 2018-04-17 00:00:00
Tasks: Cell Segmentation, GPU, Segmentation

Cell segmentation in microscopy is a challenging problem, since cells are
often asymmetric and densely packed. This becomes particularly challenging for
extremely large images, since manual intervention and processing time can make
segmentation intractable. In this paper, we present an efficient and highly
parallel formulation for symmetric three-dimensional (3D) contour evolution
that extends previous work on fast two-dimensional active contours. We provide
a formulation for optimization on 3D images, as well as a strategy for
accelerating computation on consumer graphics hardware. The proposed software
takes advantage of Monte-Carlo sampling schemes in order to speed up
convergence and reduce thread divergence. Experimental results show that this
method provides superior performance for large 2D and 3D cell segmentation
tasks when compared to existing methods on large 3D brain images.
PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning
Authors: Yunbo Wang, Zhifeng Gao, Mingsheng Long, Jian-Min Wang, Philip S. Yu
Date: 2018-04-17 00:00:00
Proceeding: ICML 2018 7
Tasks: Video Prediction

We present PredRNN++, an improved recurrent network for video predictive
learning. In pursuit of a greater spatiotemporal modeling capability, our
approach increases the transition depth between adjacent states by leveraging a
novel recurrent unit, which is named Causal LSTM for re-organizing the spatial
and temporal memories in a cascaded mechanism. However, there is still a
dilemma in video predictive learning: increasingly deep-in-time models have
been designed for capturing complex variations, while introducing more
difficulties in the gradient back-propagation. To alleviate this undesirable
effect, we propose a Gradient Highway architecture, which provides alternative
shorter routes for gradient flows from outputs back to long-range inputs. This
architecture works seamlessly with causal LSTMs, enabling PredRNN++ to capture
short-term and long-term dependencies adaptively. We assess our model on both
synthetic and real video datasets, showing its ability to ease the vanishing
gradient problem and yield state-of-the-art prediction results even in a
difficult objects occlusion scenario.
Efficient Solvers for Sparse Subspace Clustering
Authors: Farhad Pourkamali-Anaraki, James Folberth, Stephen Becker
Date: 2018-04-17 00:00:00
Tasks: Clustering

Sparse subspace clustering (SSC) clusters $n$ points that lie near a union of low-dimensional subspaces. The SSC model expresses each point as a linear or affine combination of the other points, using either $\ell_1$ or $\ell_0$ regularization. Using $\ell_1$ regularization results in a convex problem but requires $O(n^2)$ storage, and is typically solved by the alternating direction method of multipliers which takes $O(n^3)$ flops. The $\ell_0$ model is non-convex but only needs memory linear in $n$, and is solved via orthogonal matching pursuit and cannot handle the case of affine subspaces. This paper shows that a proximal gradient framework can solve SSC, covering both $\ell_1$ and $\ell_0$ models, and both linear and affine constraints. For both $\ell_1$ and $\ell_0$, algorithms to compute the proximity operator in the presence of affine constraints have not been presented in the SSC literature, so we derive an exact and efficient algorithm that solves the $\ell_1$ case with just $O(n^2)$ flops. In the $\ell_0$ case, our algorithm retains the low-memory overhead, and is the first algorithm to solve the SSC-$\ell_0$ model with affine constraints. Experiments show our algorithms do not rely on sensitive regularization parameters, and they are less sensitive to sparsity misspecification and high noise.
A Large Dataset for Improving Patch Matching
Authors: Rahul Mitra, Nehal Doiphode, Utkarsh Gautam, Sanath Narayan, Shuaib Ahmed, Sharat Chandran, Arjun Jain
Date: 2018-01-04 00:00:00
Tasks: Patch Matching, Retrieval

We propose a new dataset for learning local image descriptors which can be
used for significantly improved patch matching. Our proposed dataset consists
of an order of magnitude more number of scenes, images, and positive and
negative correspondences compared to the currently available Multi-View Stereo
(MVS) dataset from Brown et al. The new dataset also has better coverage of the
overall viewpoint, scale, and lighting changes in comparison to the MVS
dataset. Our dataset also provides supplementary information like RGB patches
with scale and rotations values, and intrinsic and extrinsic camera parameters
which as shown later can be used to customize training data as per application.
We train an existing state-of-the-art model on our dataset and evaluate on
publicly available benchmarks such as HPatches dataset and Strecha et
al.\cite{strecha} to quantify the image descriptor performance. Experimental
evaluations show that the descriptors trained using our proposed dataset
outperform the current state-of-the-art descriptors trained on MVS by 8%, 4%
and 10% on matching, verification and retrieval tasks respectively on the
HPatches dataset. Similarly on the Strecha dataset, we see an improvement of
3-5% for the matching task in non-planar scenes.