pwc-archive/papers-with-abstracts huggingface dataset:
Ad-Net: Audio-Visual Convolutional Neural Network for Advertisement Detection In Videos
Authors: Shervin Minaee, Imed Bouazizi, Prakash Kolan, Hossein Najafzadeh
Date: 2018-06-22 00:00:00

Personalized advertisement is a crucial task for many of the online
businesses and video broadcasters. Many of today's broadcasters use the same
commercial for all customers, but as one can imagine different viewers have
different interests and it seems reasonable to have customized commercial for
different group of people, chosen based on their demographic features, and
history. In this project, we propose a framework, which gets the broadcast
videos, analyzes them, detects the commercial and replaces it with a more
suitable commercial. We propose a two-stream audio-visual convolutional neural
network, that one branch analyzes the visual information and the other one
analyzes the audio information, and then the audio and visual embedding are
fused together, and are used for commercial detection, and content
categorization. We show that using both the visual and audio content of the
videos significantly improves the model performance for video analysis. This
network is trained on a dataset of more than 50k regular video and commercial
shots, and achieved much better performance compared to the models based on
hand-crafted features.
KinshipGAN: Synthesizing of Kinship Faces From Family Photos by Regularizing a Deep Face Network
Authors: Savas Ozkan, Akin Ozkan
Date: 2018-06-22 00:00:00
Tasks: Face Model

In this paper, we propose a kinship generator network that can synthesize a
possible child face by analyzing his/her parent's photo. For this purpose, we
focus on to handle the scarcity of kinship datasets throughout the paper by
proposing novel solutions in particular. To extract robust features, we
integrate a pre-trained face model to the kinship face generator. Moreover, the
generator network is regularized with an additional face dataset and
adversarial loss to decrease the overfitting of the limited samples. Lastly, we
adapt cycle-domain transformation to attain a more stable results. Experiments
are conducted on Families in the Wild (FIW) dataset. The experimental results
show that the contributions presented in the paper provide important
performance improvements compared to the baseline architecture and our proposed
method yields promising perceptual results.
Constructing Fast Network through Deconstruction of Convolution
Authors: Yunho Jeon, Junmo Kim
Date: 2018-05-28 00:00:00
Proceeding: NeurIPS 2018 12

Convolutional neural networks have achieved great success in various vision
tasks; however, they incur heavy resource costs. By using deeper and wider
networks, network accuracy can be improved rapidly. However, in an environment
with limited resources (e.g., mobile applications), heavy networks may not be
usable. This study shows that naive convolution can be deconstructed into a
shift operation and pointwise convolution. To cope with various convolutions,
we propose a new shift operation called active shift layer (ASL) that
formulates the amount of shift as a learnable function with shift parameters.
This new layer can be optimized end-to-end through backpropagation and it can
provide optimal shift values. Finally, we apply this layer to a light and fast
network that surpasses existing state-of-the-art networks.
Tensor Monte Carlo: particle methods for the GPU era
Authors: Laurence Aitchison
Date: 2018-06-22 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: GPU

Multi-sample, importance-weighted variational autoencoders (IWAE) give
tighter bounds and more accurate uncertainty estimates than variational
autoencoders (VAE) trained with a standard single-sample objective. However,
IWAEs scale poorly: as the latent dimensionality grows, they require
exponentially many samples to retain the benefits of importance weighting.
While sequential Monte-Carlo (SMC) can address this problem, it is
prohibitively slow because the resampling step imposes sequential structure
which cannot be parallelised, and moreover, resampling is non-differentiable
which is problematic when learning approximate posteriors. To address these
issues, we developed tensor Monte-Carlo (TMC) which gives exponentially many
importance samples by separately drawing $K$ samples for each of the $n$ latent
variables, then averaging over all $K^n$ possible combinations. While the sum
over exponentially many terms might seem to be intractable, in many cases it
can be computed efficiently as a series of tensor inner-products. We show that
TMC is superior to IWAE on a generative model with multiple stochastic layers
trained on the MNIST handwritten digit database, and we show that TMC can be
combined with standard variance reduction techniques.
Meta-Learning by the Baldwin Effect
Authors: Chrisantha Thomas Fernando, Jakub Sygnowski, Simon Osindero, Jane Wang, Tom Schaul, Denis Teplyashin, Pablo Sprechmann, Alexander Pritzel, Andrei A. Rusu
Date: 2018-06-06 00:00:00
Tasks: Meta-Learning, Reinforcement Learning

The scope of the Baldwin effect was recently called into question by two
papers that closely examined the seminal work of Hinton and Nowlan. To this
date there has been no demonstration of its necessity in empirically
challenging tasks. Here we show that the Baldwin effect is capable of evolving
few-shot supervised and reinforcement learning mechanisms, by shaping the
hyperparameters and the initial parameters of deep learning algorithms.
Furthermore it can genetically accommodate strong learning biases on the same
set of problems as a recent machine learning algorithm called MAML "Model
Agnostic Meta-Learning" which uses second-order gradients instead of evolution
to learn a set of reference parameters (initial weights) that can allow rapid
adaptation to tasks sampled from a distribution. Whilst in simple cases MAML is
more data efficient than the Baldwin effect, the Baldwin effect is more general
in that it does not require gradients to be backpropagated to the reference
parameters or hyperparameters, and permits effectively any number of gradient
updates in the inner loop. The Baldwin effect learns strong learning dependent
biases, rather than purely genetically accommodating fixed behaviours in a
learning independent manner.
Point cloud segmentation using hierarchical tree for architectural models
Authors: Omair Hassaan, Abeera Shamail, Zain Butt, Murtaza Taj
Date: 2018-06-22 00:00:00
Tasks: Point Cloud Segmentation, Segmentation

Recent developments in the 3D scanning technologies have made the generation
of highly accurate 3D point clouds relatively easy but the segmentation of
these point clouds remains a challenging area. A number of techniques have set
precedent of either planar or primitive based segmentation in literature. In
this work, we present a novel and an effective primitive based point cloud
segmentation algorithm. The primary focus, i.e. the main technical contribution
of our method is a hierarchical tree which iteratively divides the point cloud
into segments. This tree uses an exclusive energy function and a 3D
convolutional neural network, HollowNets to classify the segments. We test the
efficacy of our proposed approach using both real and synthetic data obtaining
an accuracy greater than 90% for domes and minarets.
Continuous Learning in Single-Incremental-Task Scenarios
Authors: Davide Maltoni, Vincenzo Lomonaco
Date: 2018-06-22 00:00:00
Tasks: class-incremental learning, Class Incremental Learning, Incremental Learning

It was recently shown that architectural, regularization and rehearsal
strategies can be used to train deep models sequentially on a number of
disjoint tasks without forgetting previously acquired knowledge. However, these
strategies are still unsatisfactory if the tasks are not disjoint but
constitute a single incremental task (e.g., class-incremental learning). In
this paper we point out the differences between multi-task and
single-incremental-task scenarios and show that well-known approaches such as
LWF, EWC and SI are not ideal for incremental task scenarios. A new approach,
denoted as AR1, combining architectural and regularization strategies is then
specifically proposed. AR1 overhead (in term of memory and computation) is very
small thus making it suitable for online learning. When tested on CORe50 and
iCIFAR-100, AR1 outperformed existing regularization strategies by a good
margin.
An accurate retrieval through R-MAC+ descriptors for landmark recognition
Authors: Federico Magliani, Andrea Prati
Date: 2018-06-22 00:00:00
Tasks: Landmark Recognition, Retrieval

The landmark recognition problem is far from being solved, but with the use
of features extracted from intermediate layers of Convolutional Neural Networks
(CNNs), excellent results have been obtained. In this work, we propose some
improvements on the creation of R-MAC descriptors in order to make the
newly-proposed R-MAC+ descriptors more representative than the previous ones.
However, the main contribution of this paper is a novel retrieval technique,
that exploits the fine representativeness of the MAC descriptors of the
database images. Using this descriptors called "db regions" during the
retrieval stage, the performance is greatly improved. The proposed method is
tested on different public datasets: Oxford5k, Paris6k and Holidays. It
outperforms the state-of-the- art results on Holidays and reached excellent
results on Oxford5k and Paris6k, overcame only by approaches based on
fine-tuning strategies.
Deep Spectral Convolution Network for HyperSpectral Unmixing
Authors: Savas Ozkan, Gozde Bozdagi Akar
Date: 2018-06-22 00:00:00
Tasks: Hyperspectral Unmixing

In this paper, we propose a novel hyperspectral unmixing technique based on
deep spectral convolution networks (DSCN). Particularly, three important
contributions are presented throughout this paper. First, fully-connected
linear operation is replaced with spectral convolutions to extract local
spectral characteristics from hyperspectral signatures with a deeper network
architecture. Second, instead of batch normalization, we propose a spectral
normalization layer which improves the selectivity of filters by normalizing
their spectral responses. Third, we introduce two fusion configurations that
produce ideal abundance maps by using the abstract representations computed
from previous layers. In experiments, we use two real datasets to evaluate the
performance of our method with other baseline techniques. The experimental
results validate that the proposed method outperforms baselines based on Root
Mean Square Error (RMSE).
The Temporal Singularity: time-accelerated simulated civilizations and their implications
Authors: Giacomo Spigler
Date: 2018-06-22 00:00:00

Provided significant future progress in artificial intelligence and
computing, it may ultimately be possible to create multiple Artificial General
Intelligences (AGIs), and possibly entire societies living within simulated
environments. In that case, it should be possible to improve the problem
solving capabilities of the system by increasing the speed of the simulation.
If a minimal simulation with sufficient capabilities is created, it might
manage to increase its own speed by accelerating progress in science and
technology, in a way similar to the Technological Singularity. This may
ultimately lead to large simulated civilizations unfolding at extreme temporal
speedups, achieving what from the outside would look like a Temporal
Singularity. Here we discuss the feasibility of the minimal simulation and the
potential advantages, dangers, and connection to the Fermi paradox of the
Temporal Singularity. The medium-term importance of the topic derives from the
amount of computational power required to start the process, which could be
available within the next decades, making the Temporal Singularity
theoretically possible before the end of the century.
Learning-to-Ask: Knowledge Acquisition via 20 Questions
Authors: Yihong Chen, Bei Chen, Xuguang Duan, Jian-Guang Lou, Yue Wang, Wenwu Zhu, Yong Cao
Date: 2018-06-22 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning

Almost all the knowledge empowered applications rely upon accurate knowledge,
which has to be either collected manually with high cost, or extracted
automatically with unignorable errors. In this paper, we study 20 Questions, an
online interactive game where each question-response pair corresponds to a fact
of the target entity, to acquire highly accurate knowledge effectively with
nearly zero labor cost. Knowledge acquisition via 20 Questions predominantly
presents two challenges to the intelligent agent playing games with human
players. The first one is to seek enough information and identify the target
entity with as few questions as possible, while the second one is to leverage
the remaining questioning opportunities to acquire valuable knowledge
effectively, both of which count on good questioning strategies. To address
these challenges, we propose the Learning-to-Ask (LA) framework, within which
the agent learns smart questioning strategies for information seeking and
knowledge acquisition by means of deep reinforcement learning and generalized
matrix factorization respectively. In addition, a Bayesian approach to
represent knowledge is adopted to ensure robustness to noisy user responses.
Simulating experiments on real data show that LA is able to equip the agent
with effective questioning strategies, which result in high winning rates and
rapid knowledge acquisition. Moreover, the questioning strategies for
information seeking and knowledge acquisition boost the performance of each
other, allowing the agent to start with a relatively small knowledge set and
quickly improve its knowledge base in the absence of constant human
supervision.
Analysis of Evolutionary Algorithms in Dynamic and Stochastic Environments
Authors: Vahid Roostapour, Mojgan Pourhassan, Frank Neumann
Date: 2018-06-22 00:00:00
Tasks: Evolutionary Algorithms

Many real-world optimization problems occur in environments that change
dynamically or involve stochastic components. Evolutionary algorithms and other
bio-inspired algorithms have been widely applied to dynamic and stochastic
problems. This survey gives an overview of major theoretical developments in
the area of runtime analysis for these problems. We review recent theoretical
studies of evolutionary algorithms and ant colony optimization for problems
where the objective functions or the constraints change over time. Furthermore,
we consider stochastic problems under various noise models and point out some
directions for future research.
Game AI Research with Fast Planet Wars Variants
Authors: Simon M. Lucas
Date: 2018-06-22 00:00:00

This paper describes a new implementation of Planet Wars, designed from the
outset for Game AI research. The skill-depth of the game makes it a challenge
for game-playing agents, and the speed of more than 1 million game ticks per
second enables rapid experimentation and prototyping. The parameterised nature
of the game together with an interchangeable actuator model make it well suited
to automated game tuning. The game is designed to be fun to play for humans,
and is directly playable by General Video Game AI agents.
Visualizing and Understanding Deep Neural Networks in CTR Prediction
Authors: Lin Guo, Hui Ye, Wenbo Su, Henhuan Liu, Kai Sun, Hang Xiang
Date: 2018-06-22 00:00:00
Tasks: Click-Through Rate Prediction

Although deep learning techniques have been successfully applied to many
tasks, interpreting deep neural network models is still a big challenge to us.
Recently, many works have been done on visualizing and analyzing the mechanism
of deep neural networks in the areas of image processing and natural language
processing. In this paper, we present our approaches to visualize and
understand deep neural networks for a very important commercial task--CTR
(Click-through rate) prediction. We conduct experiments on the productive data
from our online advertising system with daily varying distribution. To
understand the mechanism and the performance of the model, we inspect the
model's inner status at neuron level. Also, a probe approach is implemented to
measure the layer-wise performance of the model. Moreover, to measure the
influence from the input features, we calculate saliency scores based on the
back-propagated gradients. Practical applications are also discussed, for
example, in understanding, monitoring, diagnosing and refining models and
algorithms.
Focusing on What is Relevant: Time-Series Learning and Understanding using Attention
Authors: Phongtharin Vinayavekhin, Subhajit Chaudhury, Asim Munawar, Don Joven Agravante, Giovanni De Magistris, Daiki Kimura, Ryuki Tachibana
Date: 2018-06-22 00:00:00
Tasks: General Classification, Time Series, Time Series Analysis

This paper is a contribution towards interpretability of the deep learning
models in different applications of time-series. We propose a temporal
attention layer that is capable of selecting the relevant information to
perform various tasks, including data completion, key-frame detection and
classification. The method uses the whole input sequence to calculate an
attention value for each time step. This results in more focused attention
values and more plausible visualisation than previous methods. We apply the
proposed method to three different tasks. Experimental results show that the
proposed network produces comparable results to a state of the art. In
addition, the network provides better interpretability of the decision, that
is, it generates more significant attention weight to related frames compared
to similar techniques attempted in the past.
Efficient Semantic Segmentation using Gradual Grouping
Authors: Nikitha Vallurupalli, Sriharsha Annamaneni, Girish Varma, C. V. Jawahar, Manu Mathew, Soyeb Nagori
Date: 2018-06-22 00:00:00
Tasks: Decoder, Real-Time Semantic Segmentation, Segmentation, Semantic Segmentation

Deep CNNs for semantic segmentation have high memory and run time
requirements. Various approaches have been proposed to make CNNs efficient like
grouped, shuffled, depth-wise separable convolutions. We study the
effectiveness of these techniques on a real-time semantic segmentation
architecture like ERFNet for improving run time by over 5X. We apply these
techniques to CNN layers partially or fully and evaluate the testing accuracies
on Cityscapes dataset. We obtain accuracy vs parameters/FLOPs trade offs,
giving accuracy scores for models that can run under specified runtime budgets.
We further propose a novel training procedure which starts out with a dense
convolution but gradually evolves towards a grouped convolution. We show that
our proposed training method and efficient architecture design can improve
accuracies by over 8% with depth wise separable convolutions applied on the
encoder of ERFNet and attaching a light weight decoder. This results in a model
which has a 5X improvement in FLOPs while only suffering a 4% degradation in
accuracy with respect to ERFNet.
Virtual Codec Supervised Re-Sampling Network for Image Compression
Authors: Lijun Zhao, Huihui Bai, Anhong Wang, Yao Zhao
Date: 2018-06-22 00:00:00
Tasks: Dimensionality Reduction, Image Compression, Quantization

In this paper, we propose an image re-sampling compression method by learning
virtual codec network (VCN) to resolve the non-differentiable problem of
quantization function for image compression. Here, the image re-sampling not
only refers to image full-resolution re-sampling but also low-resolution
re-sampling. We generalize this method for standard-compliant image compression
(SCIC) framework and deep neural networks based compression (DNNC) framework.
Specifically, an input image is measured by re-sampling network (RSN) network
to get re-sampled vectors. Then, these vectors are directly quantized in the
feature space in SCIC, or discrete cosine transform coefficients of these
vectors are quantized to further improve coding efficiency in DNNC. At the
encoder, the quantized vectors or coefficients are losslessly compressed by
arithmetic coding. At the receiver, the decoded vectors are utilized to restore
input image by image decoder network (IDN). In order to train RSN network and
IDN network together in an end-to-end fashion, our VCN network intimates
projection from the re-sampled vectors to the IDN-decoded image. As a result,
gradients from IDN network to RSN network can be approximated by VCN network's
gradient. Because dimension reduction can be further achieved by quantization
in some dimensional space after image re-sampling within auto-encoder
architecture, we can well initialize our networks from pre-trained auto-encoder
networks. Through extensive experiments and analysis, it is verified that the
proposed method has more effectiveness and versatility than many
state-of-the-art approaches.
Global Semantic Consistency for Zero-Shot Learning
Authors: Fan Wu, Kai Tian, Jihong Guan, Shuigeng Zhou
Date: 2018-06-22 00:00:00
Tasks: Attribute, Generalized Zero-Shot Learning, Novelty Detection, Zero-Shot Learning

In image recognition, there are many cases where training samples cannot
cover all target classes. Zero-shot learning (ZSL) utilizes the class semantic
information to classify samples of the unseen categories that have no
corresponding samples contained in the training set. In this paper, we propose
an end-to-end framework, called Global Semantic Consistency Network (GSC-Net
for short), which makes complete use of the semantic information of both seen
and unseen classes, to support effective zero-shot learning. We also adopt a
soft label embedding loss to further exploit the semantic relationships among
classes. To adapt GSC-Net to a more practical setting, Generalized Zero-shot
Learning (GZSL), we introduce a parametric novelty detection mechanism. Our
approach achieves the state-of-the-art performance on both ZSL and GZSL tasks
over three visual attribute datasets, which validates the effectiveness and
advantage of the proposed framework.
Visual-Inertial Object Detection and Mapping
Authors: Xiaohan Fei, Stefano Soatto
Date: 2018-06-22 00:00:00
Proceeding: ECCV 2018 9
Tasks: Object, object-detection, Object Detection

We present a method to populate an unknown environment with models of
previously seen objects, placed in a Euclidean reference frame that is inferred
causally and on-line using monocular video along with inertial sensors. The
system we implement returns a sparse point cloud for the regions of the scene
that are visible but not recognized as a previously seen object, and a detailed
object model and its pose in the Euclidean frame otherwise. The system includes
bottom-up and top-down components, whereby deep networks trained for detection
provide likelihood scores for object hypotheses provided by a nonlinear filter,
whose state serves as memory. Additional networks provide likelihood scores for
edges, which complements detection networks trained to be invariant to small
deformations. We test our algorithm on existing datasets, and also introduce
the VISMA dataset, that provides ground truth pose, point-cloud map, and object
models, along with time-stamped inertial measurements.