pwc-archive/papers-with-abstracts huggingface dataset:
Efficient Neural Network Compression
Authors: Hyeji Kim, Muhammad Umar Karim Khan, Chong-Min Kyung
Date: 2018-11-30 00:00:00
Proceeding: CVPR 2019 6
Tasks: CPU, Efficient Neural Network, Neural Network Compression

Network compression reduces the computational complexity and memory
consumption of deep neural networks by reducing the number of parameters. In
SVD-based network compression, the right rank needs to be decided for every
layer of the network. In this paper, we propose an efficient method for
obtaining the rank configuration of the whole network. Unlike previous methods
which consider each layer separately, our method considers the whole network to
choose the right rank configuration. We propose novel accuracy metrics to
represent the accuracy and complexity relationship for a given neural network.
We use these metrics in a non-iterative fashion to obtain the right rank
configuration which satisfies the constraints on FLOPs and memory while
maintaining sufficient accuracy. Experiments show that our method provides
better compromise between accuracy and computational complexity/memory
consumption while performing compression at much higher speed. For VGG-16 our
network can reduce the FLOPs by 25% and improve accuracy by 0.7% compared to
the baseline, while requiring only 3 minutes on a CPU to search for the right
rank configuration. Previously, similar results were achieved in 4 hours with 8
GPUs. The proposed method can be used for lossless compression of a neural
network as well. The better accuracy and complexity compromise, as well as the
extremely fast speed of our method makes it suitable for neural network
compression.
Learning From Weights: A Cost-Sensitive Approach For Ad Retrieval
Authors: Nikit Begwani, Shrutendra Harsola, Rahul Agrawal
Date: 2018-11-30 00:00:00
Tasks: Product Recommendation, Retrieval

Retrieval models such as CLSM is trained on click-through data which treats
each clicked query-document pair as equivalent. While training on click-through
data is reasonable, this paper argues that it is sub-optimal because of its
noisy and long-tail nature (especially for sponsored search). In this paper, we
discuss the impact of incorporating or disregarding the long tail pairs in the
training set. Also, we propose a weighing based strategy using which we can
learn semantic representations for tail pairs without compromising the quality
of retrieval. We conducted our experiments on Bing sponsored search and also on
Amazon product recommendation to demonstrate that the methodology is domain
agnostic.
  Online A/B testing on live search engine traffic showed improvements in
clicks (11.8\% higher CTR) and as well as improvement in quality (8.2\% lower
bounce rate) when compared to the unweighted model. We also conduct the
experiment on Amazon Product Recommendation data where we see slight
improvements in NDCG Scores calculated by retrieving among co-purchased
product.
Cross-database non-frontal facial expression recognition based on transductive deep transfer learning
Authors: Keyu Yan, Wenming Zheng, Tong Zhang, Yuan Zong, Zhen Cui
Date: 2018-11-30 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER), Transfer Learning

Cross-database non-frontal expression recognition is a very meaningful but
rather difficult subject in the fields of computer vision and affect computing.
In this paper, we proposed a novel transductive deep transfer learning
architecture based on widely used VGGface16-Net for this problem. In this
framework, the VGGface16-Net is used to jointly learn an common optimal
nonlinear discriminative features from the non-frontal facial expression
samples between the source and target databases and then we design a novel
transductive transfer layer to deal with the cross-database non-frontal facial
expression classification task. In order to validate the performance of the
proposed transductive deep transfer learning networks, we present extensive
crossdatabase experiments on two famous available facial expression databases,
namely the BU-3DEF and the Multi-PIE database. The final experimental results
show that our transductive deep transfer network outperforms the
state-of-the-art cross-database facial expression recognition methods.
From Known to the Unknown: Transferring Knowledge to Answer Questions about Novel Visual and Semantic Concepts
Authors: Moshiur R. Farazi, Salman H. Khan, Nick Barnes
Date: 2018-11-30 00:00:00
Tasks: Novel Concepts, Question Answering, Retrieval, Visual Question Answering, Visual Question Answering (VQA)

Current Visual Question Answering (VQA) systems can answer intelligent
questions about `Known' visual content. However, their performance drops
significantly when questions about visually and linguistically `Unknown'
concepts are presented during inference (`Open-world' scenario). A practical
VQA system should be able to deal with novel concepts in real world settings.
To address this problem, we propose an exemplar-based approach that transfers
learning (i.e., knowledge) from previously `Known' concepts to answer questions
about the `Unknown'. We learn a highly discriminative joint embedding space,
where visual and semantic features are fused to give a unified representation.
Once novel concepts are presented to the model, it looks for the closest match
from an exemplar set in the joint embedding space. This auxiliary information
is used alongside the given Image-Question pair to refine visual attention in a
hierarchical fashion. Since handling the high dimensional exemplars on large
datasets can be a significant challenge, we introduce an efficient matching
scheme that uses a compact feature description for search and retrieval. To
evaluate our model, we propose a new split for VQA, separating Unknown visual
and semantic concepts from the training set. Our approach shows significant
improvements over state-of-the-art VQA models on the proposed Open-World VQA
dataset and standard VQA datasets.
Model-blind Video Denoising Via Frame-to-frame Training
Authors: Thibaud Ehret, Axel Davy, Jean-Michel Morel, Gabriele Facciolo, Pablo Arias
Date: 2018-11-30 00:00:00
Proceeding: CVPR 2019 6
Tasks: Denoising, Video Denoising

Modeling the processing chain that has produced a video is a difficult reverse engineering task, even when the camera is available. This makes model based video processing a still more complex task. In this paper we propose a fully blind video denoising method, with two versions off-line and on-line. This is achieved by fine-tuning a pre-trained AWGN denoising network to the video with a novel frame-to-frame training strategy. Our denoiser can be used without knowledge of the origin of the video or burst and the post processing steps applied from the camera sensor. The on-line process only requires a couple of frames before achieving visually-pleasing results for a wide range of perturbations. It nonetheless reaches state of the art performance for standard Gaussian noise, and can be used off-line with still better performance.
Non-Local Video Denoising by CNN
Authors: Axel Davy, Thibaud Ehret, Jean-Michel Morel, Pablo Arias, Gabriele Facciolo
Date: 2018-11-30 00:00:00
Tasks: Denoising, Image Denoising, Video Denoising

Non-local patch based methods were until recently state-of-the-art for image denoising but are now outperformed by CNNs. Yet they are still the state-of-the-art for video denoising, as video redundancy is a key factor to attain high denoising performance. The problem is that CNN architectures are hardly compatible with the search for self-similarities. In this work we propose a new and efficient way to feed video self-similarities to a CNN. The non-locality is incorporated into the network via a first non-trainable layer which finds for each patch in the input image its most similar patches in a search region. The central values of these patches are then gathered in a feature vector which is assigned to each image pixel. This information is presented to a CNN which is trained to predict the clean image. We apply the proposed architecture to image and video denoising. For the latter patches are searched for in a 3D spatio-temporal volume. The proposed architecture achieves state-of-the-art results. To the best of our knowledge, this is the first successful application of a CNN to video denoising.
Projection Convolutional Neural Networks for 1-bit CNNs via Discrete Back Propagation
Authors: Jiaxin Gu, Ce Li, Baochang Zhang, Jungong Han, Xian-Bin Cao, Jianzhuang Liu, David Doermann
Date: 2018-11-30 00:00:00

The advancement of deep convolutional neural networks (DCNNs) has driven
significant improvement in the accuracy of recognition systems for many
computer vision tasks. However, their practical applications are often
restricted in resource-constrained environments. In this paper, we introduce
projection convolutional neural networks (PCNNs) with a discrete back
propagation via projection (DBPP) to improve the performance of binarized
neural networks (BNNs). The contributions of our paper include: 1) for the
first time, the projection function is exploited to efficiently solve the
discrete back propagation problem, which leads to a new highly compressed CNNs
(termed PCNNs); 2) by exploiting multiple projections, we learn a set of
diverse quantized kernels that compress the full-precision kernels in a more
efficient way than those proposed previously; 3) PCNNs achieve the best
classification performance compared to other state-of-the-art BNNs on the
ImageNet and CIFAR datasets.
Practical methods for graph two-sample testing
Authors: Debarghya Ghoshdastidar, Ulrike Von Luxburg
Date: 2018-11-30 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Learning Theory, Open-Ended Question Answering, Two-sample testing, Vocal Bursts Valence Prediction

Hypothesis testing for graphs has been an important tool in applied research
fields for more than two decades, and still remains a challenging problem as
one often needs to draw inference from few replicates of large graphs. Recent
studies in statistics and learning theory have provided some theoretical
insights about such high-dimensional graph testing problems, but the
practicality of the developed theoretical methods remains an open question.
  In this paper, we consider the problem of two-sample testing of large graphs.
We demonstrate the practical merits and limitations of existing theoretical
tests and their bootstrapped variants. We also propose two new tests based on
asymptotic distributions. We show that these tests are computationally less
expensive and, in some cases, more reliable than the existing methods.
Domain-Invariant Adversarial Learning for Unsupervised Domain Adaption
Authors: Yexun Zhang, Ya zhang, Yan-Feng Wang, Qi Tian
Date: 2018-11-30 00:00:00
Tasks: Domain Adaptation, Generative Adversarial Network

Unsupervised domain adaption aims to learn a powerful classifier for the
target domain given a labeled source data set and an unlabeled target data set.
To alleviate the effect of `domain shift', the major challenge in domain
adaptation, studies have attempted to align the distributions of the two
domains. Recent research has suggested that generative adversarial network
(GAN) has the capability of implicitly capturing data distribution. In this
paper, we thus propose a simple but effective model for unsupervised domain
adaption leveraging adversarial learning. The same encoder is shared between
the source and target domains which is expected to extract domain-invariant
representations with the help of an adversarial discriminator. With the labeled
source data, we introduce the center loss to increase the discriminative power
of feature learned. We further align the conditional distribution of the two
domains to enforce the discrimination of the features in the target domain.
Unlike previous studies where the source features are extracted with a fixed
pre-trained encoder, our method jointly learns feature representations of two
domains. Moreover, by sharing the encoder, the model does not need to know the
source of images during testing and hence is more widely applicable. We
evaluate the proposed method on several unsupervised domain adaption benchmarks
and achieve superior or comparable performance to state-of-the-art results.
Improving Landmark Recognition using Saliency detection and Feature classification
Authors: Akash Kumar, Sagnik Bhowmick, N. Jayanthi, S. Indu
Date: 2018-11-30 00:00:00
Tasks: Classification, General Classification, Landmark Recognition, Robust classification, Saliency Detection

Image Landmark Recognition has been one of the most sought-after
classification challenges in the field of vision and perception. After so many
years of generic classification of buildings and monuments from images, people
are now focussing upon fine-grained problems - recognizing the category of each
building or monument. We proposed an ensemble network for the purpose of
classification of Indian Landmark Images. To this end, our method gives robust
classification by ensembling the predictions from Graph-Based Visual Saliency
(GBVS) network alongwith supervised feature-based classification algorithms
such as kNN and Random Forest. The final architecture is an adaptive learning
of all the mentioned networks. The proposed network produces a reliable score
to eliminate false category cases. Evaluation of our model was done on a new
dataset, which involves challenges such as landmark clutter, variable scaling,
partial occlusion, etc.
Neural separation of observed and unobserved distributions
Authors: Tavi Halperin, Ariel Ephrat, Yedid Hoshen
Date: 2018-11-30 00:00:00
Proceeding: ICLR 2019 5
Tasks: Speaker Separation

Separating mixed distributions is a long standing challenge for machine learning and signal processing. Most current methods either rely on making strong assumptions on the source distributions or rely on having training samples of each source in the mixture. In this work, we introduce a new method---Neural Egg Separation---to tackle the scenario of extracting a signal from an unobserved distribution additively mixed with a signal from an observed distribution. Our method iteratively learns to separate the known distribution from progressively finer estimates of the unknown distribution. In some settings, Neural Egg Separation is initialization sensitive, we therefore introduce Latent Mixture Masking which ensures a good initialization. Extensive experiments on audio and image separation tasks show that our method outperforms current methods that use the same level of supervision, and often achieves similar performance to full supervision.
Document Structure Measure for Hypernym discovery
Authors: Aswin Kannan, Shanmukha C Guttula, Balaji Ganesan, Hima P Karanam, Arun Kumar
Date: 2018-11-30 00:00:00
Tasks: Hypernym Discovery, Position

Hypernym discovery is the problem of finding terms that have is-a
relationship with a given term. We introduce a new context type, and a
relatedness measure to differentiate hypernyms from other types of semantic
relationships. Our Document Structure measure is based on hierarchical position
of terms in a document, and their presence or otherwise in definition text.
This measure quantifies the document structure using multiple attributes, and
classes of weighted distance functions.
Evaluating Bayesian Deep Learning Methods for Semantic Segmentation
Authors: Jishnu Mukhoti, Yarin Gal
Date: 2018-11-30 00:00:00
Tasks: Anomaly Detection, Autonomous Driving, Deep Learning, Segmentation, Semantic Segmentation, Uncertainty Quantification

Deep learning has been revolutionary for computer vision and semantic
segmentation in particular, with Bayesian Deep Learning (BDL) used to obtain
uncertainty maps from deep models when predicting semantic classes. This
information is critical when using semantic segmentation for autonomous driving
for example. Standard semantic segmentation systems have well-established
evaluation metrics. However, with BDL's rising popularity in computer vision we
require new metrics to evaluate whether a BDL method produces better
uncertainty estimates than another method. In this work we propose three such
metrics to evaluate BDL models designed specifically for the task of semantic
segmentation. We modify DeepLab-v3+, one of the state-of-the-art deep neural
networks, and create its Bayesian counterpart using MC dropout and Concrete
dropout as inference techniques. We then compare and test these two inference
techniques on the well-known Cityscapes dataset using our suggested metrics.
Our results provide new benchmarks for researchers to compare and evaluate
their improved uncertainty quantification in pursuit of safer semantic
segmentation.
LEARN Codes: Inventing Low-latency Codes via Recurrent Neural Networks
Authors: Yihan Jiang, Hyeji Kim, Himanshu Asnani, Sreeram Kannan, Sewoong Oh, Pramod Viswanath
Date: 2018-11-30 00:00:00
Tasks: Decoder

Designing channel codes under low-latency constraints is one of the most demanding requirements in 5G standards. However, a sharp characterization of the performance of traditional codes is available only in the large block-length limit. Guided by such asymptotic analysis, code designs require large block lengths as well as latency to achieve the desired error rate. Tail-biting convolutional codes and other recent state-of-the-art short block codes, while promising reduced latency, are neither robust to channel-mismatch nor adaptive to varying channel conditions. When the codes designed for one channel (e.g.,~Additive White Gaussian Noise (AWGN) channel) are used for another (e.g.,~non-AWGN channels), heuristics are necessary to achieve non-trivial performance. In this paper, we first propose an end-to-end learned neural code, obtained by jointly designing a Recurrent Neural Network (RNN) based encoder and decoder. This code outperforms canonical convolutional code under block settings. We then leverage this experience to propose a new class of codes under low-latency constraints, which we call Low-latency Efficient Adaptive Robust Neural (LEARN) codes. These codes outperform state-of-the-art low-latency codes and exhibit robustness and adaptivity properties. LEARN codes show the potential to design new versatile and universal codes for future communications via tools of modern deep learning coupled with communication engineering insights.
Style Decomposition for Improved Neural Style Transfer
Authors: Paraskevas Pegios, Nikolaos Passalis, Anastasios Tefas
Date: 2018-11-30 00:00:00
Tasks: Style Transfer

Universal Neural Style Transfer (NST) methods are capable of performing style
transfer of arbitrary styles in a style-agnostic manner via feature transforms
in (almost) real-time. Even though their unimodal parametric style modeling
approach has been proven adequate to transfer a single style from relatively
simple images, they are usually not capable of effectively handling more
complex styles, producing significant artifacts, as well as reducing the
quality of the synthesized textures in the stylized image. To overcome these
limitations, in this paper we propose a novel universal NST approach that
separately models each sub-style that exists in a given style image (or a
collection of style images). This allows for better modeling the subtle style
differences within the same style image and then using the most appropriate
sub-style (or mixtures of different sub-styles) to stylize the content image.
The ability of the proposed approach to a) perform a wide range of different
stylizations using the sub-styles that exist in one style image, while giving
the ability to the user to appropriate mix the different sub-styles, b)
automatically match the most appropriate sub-style to different semantic
regions of the content image, improving existing state-of-the-art universal NST
approaches, and c) detecting and transferring the sub-styles from collections
of images are demonstrated through extensive experiments.
An Efficient Image Retrieval Based on Fusion of Low-Level Visual Features
Authors: Atif Nazir, Kashif Nazir
Date: 2018-11-30 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Retrieval

Due to an increase in the number of image achieves, Content-Based Image
Retrieval (CBIR) has gained attention for research community of computer
vision. The image visual contents are represented in a feature space in the
form of numerical values that is considered as a feature vector of image.
Images belonging to different classes may contain the common visuals and shapes
that can result in the closeness of computed feature space of two different
images belonging to separate classes. Due to this reason, feature extraction
and image representation is selected with appropriate features as it directly
affects the performance of image retrieval system. The commonly used visual
features are image spatial layout, color, texture and shape. Image feature
space is combined to achieve the discriminating ability that is not possible to
achieve when the features are used separately. Due to this reason, in this
paper, we aim to explore the low-level feature combination that are based on
color and shape features. We selected color moments and color histogram to
represent color while shape is represented by using invariant moments. We
selected this combination, as these features are reported intuitive, compact
and robust for image representation. We evaluated the performance of our
proposed research by using the Corel, Coil and Ground Truth (GT) image
datasets. We evaluated the proposed low-level feature fusion by calculating the
precision, recall and time required for feature extraction. The precision,
recall and feature extraction values obtained from the proposed low-level
feature fusion outperforms the existing research of CBIR.
Void Filling of Digital Elevation Models with Deep Generative Models
Authors: Konstantinos Gavriil, Georg Muntingh, Oliver J. D. Barrowclough
Date: 2018-11-30 00:00:00
Tasks: BIG-bench Machine Learning, Generative Adversarial Network, Image Inpainting

In recent years, advances in machine learning algorithms, cheap computational
resources, and the availability of big data have spurred the deep learning
revolution in various application domains. In particular, supervised learning
techniques in image analysis have led to superhuman performance in various
tasks, such as classification, localization, and segmentation, while
unsupervised learning techniques based on increasingly advanced generative
models have been applied to generate high-resolution synthetic images
indistinguishable from real images.
  In this paper we consider a state-of-the-art machine learning model for image
inpainting, namely a Wasserstein Generative Adversarial Network based on a
fully convolutional architecture with a contextual attention mechanism. We show
that this model can successfully be transferred to the setting of digital
elevation models (DEMs) for the purpose of generating semantically plausible
data for filling voids. Training, testing and experimentation is done on
GeoTIFF data from various regions in Norway, made openly available by the
Norwegian Mapping Authority.