pwc-archive/papers-with-abstracts huggingface dataset:
Exploring the Limits of Weakly Supervised Pretraining
Authors: Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe, Laurens van der Maaten
Date: 2018-05-02 00:00:00
Proceeding: ECCV 2018 9
Tasks: General Classification, image-classification, Image Classification, object-detection, Object Detection, Transfer Learning

State-of-the-art visual perception models for a wide range of tasks rely on
supervised pretraining. ImageNet classification is the de facto pretraining
task for these models. Yet, ImageNet is now nearly ten years old and is by
modern standards "small". Even so, relatively little is known about the
behavior of pretraining with datasets that are multiple orders of magnitude
larger. The reasons are obvious: such datasets are difficult to collect and
annotate. In this paper, we present a unique study of transfer learning with
large convolutional networks trained to predict hashtags on billions of social
media images. Our experiments demonstrate that training for large-scale hashtag
prediction leads to excellent results. We show improvements on several image
classification and object detection tasks, and report the highest ImageNet-1k
single-crop, top-1 accuracy to date: 85.4% (97.6% top-5). We also perform
extensive experiments that provide novel empirical data on the relationship
between large-scale pretraining and transfer learning performance.
Multi-Resolution Multi-Modal Sensor Fusion For Remote Sensing Data With Label Uncertainty
Authors: Xiaoxiao Du, Alina Zare
Date: 2018-05-02 00:00:00
Tasks: Scene Understanding, Sensor Fusion

In remote sensing, each sensor can provide complementary or reinforcing information. It is valuable to fuse outputs from multiple sensors to boost overall performance. Previous supervised fusion methods often require accurate labels for each pixel in the training data. However, in many remote sensing applications, pixel-level labels are difficult or infeasible to obtain. In addition, outputs from multiple sensors often have different resolution or modalities. For example, rasterized hyperspectral imagery presents data in a pixel grid while airborne Light Detection and Ranging (LiDAR) generates dense three-dimensional (3D) point clouds. It is often difficult to directly fuse such multi-modal, multi-resolution data. To address these challenges, we present a novel Multiple Instance Multi-Resolution Fusion (MIMRF) framework that can fuse multi-resolution and multi-modal sensor outputs while learning from automatically-generated, imprecisely-labeled data. Experiments were conducted on the MUUFL Gulfport hyperspectral and LiDAR data set and a remotely-sensed soybean and weed data set. Results show improved, consistent performance on scene understanding and agricultural applications when compared to traditional fusion methods.
Lidar Cloud Detection with Fully Convolutional Networks
Authors: Erol Cromwell, Donna Flynn
Date: 2018-05-02 00:00:00
Tasks: Cloud Detection, General Classification

In this contribution, we present a novel approach for segmenting laser radar
(lidar) imagery into geometric time-height cloud locations with a fully
convolutional network (FCN). We describe a semi-supervised learning method to
train the FCN by: pre-training the classification layers of the FCN with
image-level annotations, pre-training the entire FCN with the cloud locations
of the MPLCMASK cloud mask algorithm, and fully supervised learning with
hand-labeled cloud locations. We show the model achieves higher levels of cloud
identification compared to the cloud mask algorithm implementation.
GXNOR-Net: Training deep neural networks with ternary weights and activations without full-precision memory under a unified discretization framework
Authors: Lei Deng, Peng Jiao, Jing Pei, Zhenzhi Wu, Guoqi Li
Date: 2017-05-25 00:00:00

There is a pressing need to build an architecture that could subsume these
networks under a unified framework that achieves both higher performance and
less overhead. To this end, two fundamental issues are yet to be addressed. The
first one is how to implement the back propagation when neuronal activations
are discrete. The second one is how to remove the full-precision hidden weights
in the training phase to break the bottlenecks of memory/computation
consumption. To address the first issue, we present a multi-step neuronal
activation discretization method and a derivative approximation technique that
enable the implementing the back propagation algorithm on discrete DNNs. While
for the second issue, we propose a discrete state transition (DST) methodology
to constrain the weights in a discrete space without saving the hidden weights.
Through this way, we build a unified framework that subsumes the binary or
ternary networks as its special cases, and under which a heuristic algorithm is
provided at the website https://github.com/AcrossV/Gated-XNOR. More
particularly, we find that when both the weights and activations become ternary
values, the DNNs can be reduced to sparse binary networks, termed as gated XNOR
networks (GXNOR-Nets) since only the event of non-zero weight and non-zero
activation enables the control gate to start the XNOR logic operations in the
original binary networks. This promises the event-driven hardware design for
efficient mobile intelligence. We achieve advanced performance compared with
state-of-the-art algorithms. Furthermore, the computational sparsity and the
number of states in the discrete space can be flexibly modified to make it
suitable for various hardware platforms.
Altered Fingerprints: Detection and Localization
Authors: Elham Tabassi, Tarang Chugh, Debayan Deb, Anil K. Jain
Date: 2018-05-02 00:00:00
Tasks: Friction, Generative Adversarial Network

Fingerprint alteration, also referred to as obfuscation presentation attack,
is to intentionally tamper or damage the real friction ridge patterns to avoid
identification by an AFIS. This paper proposes a method for detection and
localization of fingerprint alterations. Our main contributions are: (i) design
and train CNN models on fingerprint images and minutiae-centered local patches
in the image to detect and localize regions of fingerprint alterations, and
(ii) train a Generative Adversarial Network (GAN) to synthesize altered
fingerprints whose characteristics are similar to true altered fingerprints. A
successfully trained GAN can alleviate the limited availability of altered
fingerprint images for research. A database of 4,815 altered fingerprints from
270 subjects, and an equal number of rolled fingerprint images are used to
train and test our models. The proposed approach achieves a True Detection Rate
(TDR) of 99.24% at a False Detection Rate (FDR) of 2%, outperforming published
results. The synthetically generated altered fingerprint dataset will be
open-sourced.
Off the Beaten Track: Using Deep Learning to Interpolate Between Music Genres
Authors: Tijn Borghuis, Alessandro Tibo, Simone Conforti, Luca Canciello, Lorenzo Brusci, Paolo Frasconi
Date: 2018-04-25 00:00:00

We describe a system based on deep learning that generates drum patterns in
the electronic dance music domain. Experimental results reveal that generated
patterns can be employed to produce musically sound and creative transitions
between different genres, and that the process of generation is of interest to
practitioners in the field.
Feasibility Based Large Margin Nearest Neighbor Metric Learning
Authors: Babak Hosseini, Barbara Hammer
Date: 2016-10-18 00:00:00
Tasks: Metric Learning

Large margin nearest neighbor (LMNN) is a metric learner which optimizes the
performance of the popular $k$NN classifier. However, its resulting metric
relies on pre-selected target neighbors. In this paper, we address the
feasibility of LMNN's optimization constraints regarding these target points,
and introduce a mathematical measure to evaluate the size of the feasible
region of the optimization problem. We enhance the optimization framework of
LMNN by a weighting scheme which prefers data triplets which yield a larger
feasible region. This increases the chances to obtain a good metric as the
solution of LMNN's problem. We evaluate the performance of the resulting
feasibility-based LMNN algorithm using synthetic and real datasets. The
empirical results show an improved accuracy for different types of datasets in
comparison to regular LMNN.
Improving Coverage and Runtime Complexity for Exact Inference in Non-Projective Transition-Based Dependency Parsers
Authors: Tianze Shi, Carlos Gómez-Rodríguez, Lillian Lee
Date: 2018-04-27 00:00:00
Proceeding: NAACL 2018 6

We generalize Cohen, G\'omez-Rodr\'iguez, and Satta's (2011) parser to a
family of non-projective transition-based dependency parsers allowing
polynomial-time exact inference. This includes novel parsers with better
coverage than Cohen et al. (2011), and even a variant that reduces time
complexity to $O(n^6)$, improving over the known bounds in exact inference for
non-projective transition-based parsing. We hope that this piece of theoretical
work inspires design of novel transition systems with better coverage and
better run-time guarantees.
  Code available at https://github.com/tzshi/nonproj-dp-variants-naacl2018
Images & Recipes: Retrieval in the cooking context
Authors: Micael Carvalho, Rémi Cadène, David Picard, Laure Soulier, Matthieu Cord
Date: 2018-05-02 00:00:00
Tasks: BIG-bench Machine Learning, Retrieval

Recent advances in the machine learning community allowed different use cases
to emerge, as its association to domains like cooking which created the
computational cuisine. In this paper, we tackle the picture-recipe alignment
problem, having as target application the large-scale retrieval task (finding a
recipe given a picture, and vice versa). Our approach is validated on the
Recipe1M dataset, composed of one million image-recipe pairs and additional
class information, for which we achieve state-of-the-art results.
AI safety via debate
Authors: Geoffrey Irving, Paul Christiano, Dario Amodei
Date: 2018-05-02 00:00:00

To make AI systems broadly useful for challenging real-world tasks, we need
them to learn complex human goals and preferences. One approach to specifying
complex goals asks humans to judge during training which agent behaviors are
safe and useful, but this approach can fail if the task is too complicated for
a human to directly judge. To help address this concern, we propose training
agents via self play on a zero sum debate game. Given a question or proposed
action, two agents take turns making short statements up to a limit, then a
human judges which of the agents gave the most true, useful information. In an
analogy to complexity theory, debate with optimal play can answer any question
in PSPACE given polynomial time judges (direct judging answers only NP
questions). In practice, whether debate works involves empirical questions
about humans and the tasks we want AIs to perform, plus theoretical questions
about the meaning of AI alignment. We report results on an initial MNIST
experiment where agents compete to convince a sparse classifier, boosting the
classifier's accuracy from 59.4% to 88.9% given 6 pixels and from 48.2% to
85.2% given 4 pixels. Finally, we discuss theoretical and practical aspects of
the debate model, focusing on potential weaknesses as the model scales up, and
we propose future human and computer experiments to test these properties.
Unsupervised Cross-Lingual Information Retrieval using Monolingual Data Only
Authors: Robert Litschko, Goran Glavaš, Simone Paolo Ponzetto, Ivan Vulić
Date: 2018-05-02 00:00:00
Tasks: Cross-Lingual Information Retrieval, Information Retrieval, Retrieval

We propose a fully unsupervised framework for ad-hoc cross-lingual
information retrieval (CLIR) which requires no bilingual data at all. The
framework leverages shared cross-lingual word embedding spaces in which terms,
queries, and documents can be represented, irrespective of their actual
language. The shared embedding spaces are induced solely on the basis of
monolingual corpora in two languages through an iterative process based on
adversarial neural networks. Our experiments on the standard CLEF CLIR
collections for three language pairs of varying degrees of language similarity
(English-Dutch/Italian/Finnish) demonstrate the usefulness of the proposed
fully unsupervised approach. Our CLIR models with unsupervised cross-lingual
embeddings outperform baselines that utilize cross-lingual embeddings induced
relying on word-level and document-level alignments. We then demonstrate that
further improvements can be achieved by unsupervised ensemble CLIR models. We
believe that the proposed framework is the first step towards development of
effective CLIR models for language pairs and domains where parallel data are
scarce or non-existent.
Modelling tourism demand to Spain with machine learning techniques. The impact of forecast horizon on model selection
Authors: Oscar Claveria, Enric Monte, Salvador Torra
Date: 2018-05-02 00:00:00
Tasks: BIG-bench Machine Learning, Model Selection, regression

This study assesses the influence of the forecast horizon on the forecasting
performance of several machine learning techniques. We compare the fo recast
accuracy of Support Vector Regression (SVR) to Neural Network (NN) models,
using a linear model as a benchmark. We focus on international tourism demand
to all seventeen regions of Spain. The SVR with a Gaussian radial basis
function kernel outperforms the rest of the models for the longest forecast
horizons. We also find that machine learning methods improve their forecasting
accuracy with respect to linear models as forecast horizons increase. This
result shows the suitability of SVR for medium and long term forecasting.
Approximate Temporal Difference Learning is a Gradient Descent for Reversible Policies
Authors: Yann Ollivier
Date: 2018-05-02 00:00:00
Tasks: Reinforcement Learning

In reinforcement learning, temporal difference (TD) is the most direct
algorithm to learn the value function of a policy. For large or infinite state
spaces, exact representations of the value function are usually not available,
and it must be approximated by a function in some parametric family.
  However, with \emph{nonlinear} parametric approximations (such as neural
networks), TD is not guaranteed to converge to a good approximation of the true
value function within the family, and is known to diverge even in relatively
simple cases. TD lacks an interpretation as a stochastic gradient descent of an
error between the true and approximate value functions, which would provide
such guarantees.
  We prove that approximate TD is a gradient descent provided the current
policy is \emph{reversible}. This holds even with nonlinear approximations.
  A policy with transition probabilities $P(s,s')$ between states is reversible
if there exists a function $\mu$ over states such that
$\frac{P(s,s')}{P(s',s)}=\frac{\mu(s')}{\mu(s)}$. In particular, every move can
be undone with some probability. This condition is restrictive; it is
satisfied, for instance, for a navigation problem in any unoriented graph.
  In this case, approximate TD is exactly a gradient descent of the
\emph{Dirichlet norm}, the norm of the difference of \emph{gradients} between
the true and approximate value functions. The Dirichlet norm also controls the
bias of approximate policy gradient. These results hold even with no decay
factor ($\gamma=1$) and do not rely on contractivity of the Bellman operator,
thus proving stability of TD even with $\gamma=1$ for reversible policies.
Training Tips for the Transformer Model
Authors: Martin Popel, Ondřej Bojar
Date: 2018-04-01 00:00:00
Tasks: Machine Translation, model, Sentence, Translation

This article describes our experiments in neural machine translation using
the recent Tensor2Tensor framework and the Transformer sequence-to-sequence
model (Vaswani et al., 2017). We examine some of the critical parameters that
affect the final translation quality, memory usage, training stability and
training time, concluding each experiment with a set of recommendations for
fellow researchers. In addition to confirming the general mantra "more data and
larger models", we address scaling to multiple GPUs and provide practical tips
for improved training regarding batch size, learning rate, warmup steps,
maximum sentence length and checkpoint averaging. We hope that our observations
will allow others to get better results given their particular hardware and
data constraints.
Spectral clustering algorithms for the detection of clusters in block-cyclic and block-acyclic graphs
Authors: H. Van Lierde, T. W. S. Chow, J. -C. Delvenne
Date: 2018-05-02 00:00:00
Tasks: Clustering, Graph Clustering

We propose two spectral algorithms for partitioning nodes in directed graphs
respectively with a cyclic and an acyclic pattern of connection between groups
of nodes. Our methods are based on the computation of extremal eigenvalues of
the transition matrix associated to the directed graph. The two algorithms
outperform state-of-the art methods for directed graph clustering on synthetic
datasets, including methods based on blockmodels, bibliometric symmetrization
and random walks. Our algorithms have the same space complexity as classical
spectral clustering algorithms for undirected graphs and their time complexity
is also linear in the number of edges in the graph. One of our methods is
applied to a trophic network based on predator-prey relationships. It
successfully extracts common categories of preys and predators encountered in
food chains. The same method is also applied to highlight the hierarchical
structure of a worldwide network of Autonomous Systems depicting business
agreements between Internet Service Providers.
Modelling cross-dependencies between Spain's regional tourism markets with an extension of the Gaussian process regression model
Authors: Oscar Claveria, Enric Monte, Salvador Torra
Date: 2018-05-02 00:00:00
Tasks: regression

This study presents an extension of the Gaussian process regression model for
multiple-input multiple-output forecasting. This approach allows modelling the
cross-dependencies between a given set of input variables and generating a
vectorial prediction. Making use of the existing correlations in international
tourism demand to all seventeen regions of Spain, the performance of the
proposed model is assessed in a multiple-step-ahead forecasting comparison. The
results of the experiment in a multivariate setting show that the Gaussian
process regression model significantly improves the forecasting accuracy of a
multi-layer perceptron neural network used as a benchmark. The results reveal
that incorporating the connections between different markets in the modelling
process may prove very useful to refine predictions at a regional level.
A Human and Group Behaviour Simulation Evaluation Framework utilising Composition and Video Analysis
Authors: Rob Dupre, Vasileios Argyriou
Date: 2017-07-09 00:00:00

In this work we present the modular Crowd Simulation Evaluation through
Composition framework (CSEC) which provides a quantitative comparison between
different pedestrian and crowd simulation approaches. Evaluation is made based
on the comparison of source footage against synthetic video created through
novel composition techniques. The proposed framework seeks to reduce the
complexity of simulation evaluation and provide a platform from which the
comparison of differing simulation algorithms as well as parametric tuning can
be conducted to improve simulation accuracy or providing measures of similarity
between crowd simulation algorithms and source data. Through the use of
features designed to mimic the Human Visual System (HVS), specific simulation
properties can be evaluated relative to sample footage. Validation was
performed on a number of popular crowd datasets and through comparisons of
multiple pedestrian and crowd simulation algorithms.
Feature Selective Small Object Detection via Knowledge-based Recurrent Attentive Neural Network
Authors: Kai Yi, Zhiqiang Jian, Shitao Chen, Nanning Zheng
Date: 2018-03-13 00:00:00
Tasks: Autonomous Driving, Decision Making, Object, object-detection, Object Detection, Open-Ended Question Answering, Small Object Detection

At present, the performance of deep neural network in general object
detection is comparable to or even surpasses that of human beings. However, due
to the limitations of deep learning itself, the small proportion of feature
pixels, and the occurence of blur and occlusion, the detection of small objects
in complex scenes is still an open question. But we can not deny that real-time
and accurate object detection is fundamental to automatic perception and
subsequent perception-based decision-making and planning tasks of autonomous
driving.
  Considering the characteristics of small objects in autonomous driving scene,
we proposed a novel method named KB-RANN, which based on domain knowledge,
intuitive experience and feature attentive selection. It can focus on
particular parts of image features, and then it tries to stress the importance
of these features and strengthenes the learning parameters of them. Our
comparative experiments on KITTI and COCO datasets show that our proposed
method can achieve considerable results both in speed and accuracy, and can
improve the effect of small object detection through self-selection of
important features and continuous enhancement of proposed method, and deployed
it in our self-developed autonomous driving car.
Fast and accurate computation of orthogonal moments for texture analysis
Authors: C. Di Ruberto, L. Putzu, G. Rodriguez
Date: 2018-03-01 00:00:00
Tasks: General Classification, Texture Classification

In this work we describe a fast and stable algorithm for the computation of
the orthogonal moments of an image. Indeed, orthogonal moments are
characterized by a high discriminative power, but some of their possible
formulations are characterized by a large computational complexity, which
limits their real-time application. This paper describes in detail an approach
based on recurrence relations, and proposes an optimized Matlab implementation
of the corresponding computational procedure, aiming to solve the above
limitations and put at the community's disposal an efficient and easy to use
software. In our experiments we evaluate the effectiveness of the recurrence
formulation, as well as its performance for the reconstruction task, in
comparison to the closed form representation, often used in the literature. The
results show a sensible reduction in the computational complexity, together
with a greater accuracy in reconstruction. In order to assess and compare the
accuracy of the computed moments in texture analysis, we perform classification
experiments on six well-known databases of texture images. Again, the
recurrence formulation performs better in classification than the closed form
representation. More importantly, if computed from the GLCM of the image using
the proposed stable procedure, the orthogonal moments outperform in some
situations some of the most diffused state-of-the-art descriptors for texture
classification.
Text Detection and Recognition in images: A survey
Authors: Tanvi Goswami, Zankhana Barad, Prof. Nikita P. Desai
Date: 2018-03-20 00:00:00
Tasks: Survey, Text Detection

Text Detection and recognition is a one of the important aspect of image
processing. This paper analyzes and compares the methods to handle this task.
It summarizes the fundamental problems and enumerates factors that need
consideration when addressing these problems. Existing techniques are
categorized as either stepwise or integrated and sub-problems are highlighted
including digit localization, verification, segmentation and recognition.
Special issues associated with the enhancement of degraded text and the
processing of video text and multi-oriented text are also addressed. The
categories and sub-categories of text are illustrated, benchmark datasets are
enumerated, and the performance of the most representative approaches is
compared. This review also provides a fundamental comparison and analysis of
the remaining problems in the field.