pwc-archive/papers-with-abstracts huggingface dataset:
Learning a Virtual Codec Based on Deep Convolutional Neural Network to Compress Image
Authors: Lijun Zhao, Huihui Bai, Anhong Wang, Yao Zhao
Date: 2017-12-16 00:00:00
Tasks: Blocking, Image Compression, Quantization, valid

Although deep convolutional neural network has been proved to efficiently
eliminate coding artifacts caused by the coarse quantization of traditional
codec, it's difficult to train any neural network in front of the encoder for
gradient's back-propagation. In this paper, we propose an end-to-end image
compression framework based on convolutional neural network to resolve the
problem of non-differentiability of the quantization function in the standard
codec. First, the feature description neural network is used to get a valid
description in the low-dimension space with respect to the ground-truth image
so that the amount of image data is greatly reduced for storage or
transmission. After image's valid description, standard image codec such as
JPEG is leveraged to further compress image, which leads to image's great
distortion and compression artifacts, especially blocking artifacts, detail
missing, blurring, and ringing artifacts. Then, we use a post-processing neural
network to remove these artifacts. Due to the challenge of directly learning a
non-linear function for a standard codec based on convolutional neural network,
we propose to learn a virtual codec neural network to approximate the
projection from the valid description image to the post-processed compressed
image, so that the gradient could be efficiently back-propagated from the
post-processing neural network to the feature description neural network during
training. Meanwhile, an advanced learning algorithm is proposed to train our
deep neural networks for compression. Obviously, the priority of the proposed
method is compatible with standard existing codecs and our learning strategy
can be easily extended into these codecs based on convolutional neural network.
Experimental results have demonstrated the advances of the proposed method as
compared to several state-of-the-art approaches, especially at very low
bit-rate.
Fully Convolutional Multi-scale Residual DenseNets for Cardiac Segmentation and Automated Cardiac Diagnosis using Ensemble of Classifiers
Authors: Mahendra Khened, Varghese Alex Kollerathu, Ganapathy Krishnamurthi
Date: 2018-01-16 00:00:00
Tasks: Cardiac Segmentation, Diagnostic, Image Segmentation, Medical Image Analysis, Medical Image Segmentation, Segmentation, Semantic Segmentation

Deep fully convolutional neural network (FCN) based architectures have shown
great potential in medical image segmentation. However, such architectures
usually have millions of parameters and inadequate number of training samples
leading to over-fitting and poor generalization. In this paper, we present a
novel highly parameter and memory efficient FCN based architecture for medical
image analysis. We propose a novel up-sampling path which incorporates long
skip and short-cut connections to overcome the feature map explosion in FCN
like architectures. In order to processes the input images at multiple scales
and view points simultaneously, we propose to incorporate Inception module's
parallel structures. We also propose a novel dual loss function whose weighting
scheme allows to combine advantages of cross-entropy and dice loss. We have
validated our proposed network architecture on two publicly available datasets,
namely: (i) Automated Cardiac Disease Diagnosis Challenge (ACDC-2017), (ii)
Left Ventricular Segmentation Challenge (LV-2011). Our approach in ACDC-2017
challenge stands second place for segmentation and first place in automated
cardiac disease diagnosis tasks with an accuracy of 100%. In the LV-2011
challenge our approach attained 0.74 Jaccard index, which is so far the highest
published result in fully automated algorithms. From the segmentation we
extracted clinically relevant cardiac parameters and hand-crafted features
which reflected the clinical diagnostic analysis to train an ensemble system
for cardiac disease classification. Our approach combined both cardiac
segmentation and disease diagnosis into a fully automated framework which is
computational efficient and hence has the potential to be incorporated in
computer-aided diagnosis (CAD) tools for clinical application.
Towards Real-Time Advancement of Underwater Visual Quality with GAN
Authors: Xingyu Chen, Junzhi Yu, Shihan Kong, Zhengxing Wu, Xi Fang, Li Wen
Date: 2017-12-03 00:00:00

Low visual quality has prevented underwater robotic vision from a wide range of applications. Although several algorithms have been developed, real-time and adaptive methods are deficient for real-world tasks. In this paper, we address this difficulty based on generative adversarial networks (GAN), and propose a GAN-based restoration scheme (GAN-RS). In particular, we develop a multi-branch discriminator including an adversarial branch and a critic branch for the purpose of simultaneously preserving image content and removing underwater noise. In addition to adversarial learning, a novel dark channel prior loss also promotes the generator to produce realistic vision. More specifically, an underwater index is investigated to describe underwater properties, and a loss function based on the underwater index is designed to train the critic branch for underwater noise suppression. Through extensive comparisons on visual quality and feature restoration, we confirm the superiority of the proposed approach. Consequently, the GAN-RS can adaptively improve underwater visual quality in real time and induce an overall superior restoration performance. Finally, a real-world experiment is conducted on the seabed for grasping marine products, and the results are quite promising. The source code is publicly available at https://github.com/SeanChenxy/GAN_RS.
GitGraph - Architecture Search Space Creation through Frequent Computational Subgraph Mining
Authors: Kamil Bennani-Smires, Claudiu Musat, Andreea Hossmann, Michael Baeriswyl
Date: 2018-01-16 00:00:00
Tasks: Evolutionary Algorithms, Neural Architecture Search, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The dramatic success of deep neural networks across multiple application
areas often relies on experts painstakingly designing a network architecture
specific to each task. To simplify this process and make it more accessible, an
emerging research effort seeks to automate the design of neural network
architectures, using e.g. evolutionary algorithms or reinforcement learning or
simple search in a constrained space of neural modules.
  Considering the typical size of the search space (e.g. $10^{10}$ candidates
for a $10$-layer network) and the cost of evaluating a single candidate,
current architecture search methods are very restricted. They either rely on
static pre-built modules to be recombined for the task at hand, or they define
a static hand-crafted framework within which they can generate new
architectures from the simplest possible operations.
  In this paper, we relax these restrictions, by capitalizing on the collective
wisdom contained in the plethora of neural networks published in online code
repositories. Concretely, we (a) extract and publish GitGraph, a corpus of
neural architectures and their descriptions; (b) we create problem-specific
neural architecture search spaces, implemented as a textual search mechanism
over GitGraph; (c) we propose a method of identifying unique common subgraphs
within the architectures solving each problem (e.g., image processing,
reinforcement learning), that can then serve as modules in the newly created
problem specific neural search space.
Theory of Deep Learning III: explaining the non-overfitting puzzle
Authors: Tomaso Poggio, Kenji Kawaguchi, Qianli Liao, Brando Miranda, Lorenzo Rosasco, Xavier Boix, Jack Hidary, Hrushikesh Mhaskar
Date: 2017-12-30 00:00:00
Tasks: Deep Learning, General Classification

A main puzzle of deep networks revolves around the absence of overfitting
despite large overparametrization and despite the large capacity demonstrated
by zero training error on randomly labeled data. In this note, we show that the
dynamics associated to gradient descent minimization of nonlinear networks is
topologically equivalent, near the asymptotically stable minima of the
empirical error, to linear gradient system in a quadratic potential with a
degenerate (for square loss) or almost degenerate (for logistic or crossentropy
loss) Hessian. The proposition depends on the qualitative theory of dynamical
systems and is supported by numerical results. Our main propositions extend to
deep nonlinear networks two properties of gradient descent for linear networks,
that have been recently established (1) to be key to their generalization
properties: 1. Gradient descent enforces a form of implicit regularization
controlled by the number of iterations, and asymptotically converges to the
minimum norm solution for appropriate initial conditions of gradient descent.
This implies that there is usually an optimum early stopping that avoids
overfitting of the loss. This property, valid for the square loss and many
other loss functions, is relevant especially for regression. 2. For
classification, the asymptotic convergence to the minimum norm solution implies
convergence to the maximum margin solution which guarantees good classification
error for "low noise" datasets. This property holds for loss functions such as
the logistic and cross-entropy loss independently of the initial conditions.
The robustness to overparametrization has suggestive implications for the
robustness of the architecture of deep convolutional networks with respect to
the curse of dimensionality.
Empirical Explorations in Training Networks with Discrete Activations
Authors: Shumeet Baluja
Date: 2018-01-16 00:00:00
Tasks: Memorization

We present extensive experiments training and testing hidden units in deep
networks that emit only a predefined, static, number of discretized values.
These units provide benefits in real-world deployment in systems in which
memory and/or computation may be limited. Additionally, they are particularly
well suited for use in large recurrent network models that require the
maintenance of large amounts of internal state in memory. Surprisingly, we find
that despite reducing the number of values that can be represented in the
output activations from $2^{32}-2^{64}$ to between 64 and 256, there is little
to no degradation in network performance across a variety of different
settings. We investigate simple classification and regression tasks, as well as
memorization and compression problems. We compare the results with more
standard activations, such as tanh and relu. Unlike previous discretization
studies which often concentrate only on binary units, we examine the effects of
varying the number of allowed activation levels. Compared to existing
approaches for discretization, the approach presented here is both conceptually
and programatically simple, has no stochastic component, and allows the
training, testing, and usage phases to be treated in exactly the same manner.
Constraint-free Natural Image Reconstruction from fMRI Signals Based on Convolutional Neural Network
Authors: Chi Zhang, Kai Qiao, Linyuan Wang, Li Tong, Ying Zeng, Bin Yan
Date: 2018-01-16 00:00:00
Tasks: Image Reconstruction

In recent years, research on decoding brain activity based on functional
magnetic resonance imaging (fMRI) has made remarkable achievements. However,
constraint-free natural image reconstruction from brain activity is still a
challenge. The existing methods simplified the problem by using semantic prior
information or just reconstructing simple images such as letters and digitals.
Without semantic prior information, we present a novel method to reconstruct
nature images from fMRI signals of human visual cortex based on the computation
model of convolutional neural network (CNN). Firstly, we extracted the units
output of viewed natural images in each layer of a pre-trained CNN as CNN
features. Secondly, we transformed image reconstruction from fMRI signals into
the problem of CNN feature visualizations by training a sparse linear
regression to map from the fMRI patterns to CNN features. By iteratively
optimization to find the matched image, whose CNN unit features become most
similar to those predicted from the brain activity, we finally achieved the
promising results for the challenging constraint-free natural image
reconstruction. As there was no use of semantic prior information of the
stimuli when training decoding model, any category of images (not constraint by
the training set) could be reconstructed theoretically. We found that the
reconstructed images resembled the natural stimuli, especially in position and
shape. The experimental results suggest that hierarchical visual features can
effectively express the visual perception process of human brain.
OneNet: Joint Domain, Intent, Slot Prediction for Spoken Language Understanding
Authors: Young-Bum Kim, Sungjin Lee, Karl Stratos
Date: 2018-01-16 00:00:00
Tasks: Spoken Language Understanding

In practice, most spoken language understanding systems process user input in
a pipelined manner; first domain is predicted, then intent and semantic slots
are inferred according to the semantic frames of the predicted domain. The
pipeline approach, however, has some disadvantages: error propagation and lack
of information sharing. To address these issues, we present a unified neural
network that jointly performs domain, intent, and slot predictions. Our
approach adopts a principled architecture for multitask learning to fold in the
state-of-the-art models for each task. With a few more ingredients, e.g.
orthography-sensitive input encoding and curriculum training, our model
delivered significant improvements in all three tasks across all domains over
strong baselines, including one using oracle prediction for domain detection,
on real user data of a commercial personal assistant.
Adversarial Learning for Chinese NER from Crowd Annotations
Authors: YaoSheng Yang, Meishan Zhang, Wenliang Chen, Wei zhang, Haofen Wang, Min Zhang
Date: 2018-01-16 00:00:00
Tasks: Chinese Named Entity Recognition, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

To quickly obtain new labeled data, we can choose crowdsourcing as an
alternative way at lower cost in a short time. But as an exchange, crowd
annotations from non-experts may be of lower quality than those from experts.
In this paper, we propose an approach to performing crowd annotation learning
for Chinese Named Entity Recognition (NER) to make full use of the noisy
sequence labels from multiple annotators. Inspired by adversarial learning, our
approach uses a common Bi-LSTM and a private Bi-LSTM for representing
annotator-generic and -specific information. The annotator-generic information
is the common knowledge for entities easily mastered by the crowd. Finally, we
build our Chinese NE tagger based on the LSTM-CRF model. In our experiments, we
create two data sets for Chinese NER tasks from two domains. The experimental
results show that our system achieves better scores than strong baseline
systems.
Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks
Authors: Pratik Chaudhari, Stefano Soatto
Date: 2017-10-30 00:00:00
Proceeding: ICLR 2018 1
Tasks: Variational Inference

Stochastic gradient descent (SGD) is widely believed to perform implicit
regularization when used to train deep neural networks, but the precise manner
in which this occurs has thus far been elusive. We prove that SGD minimizes an
average potential over the posterior distribution of weights along with an
entropic regularization term. This potential is however not the original loss
function in general. So SGD does perform variational inference, but for a
different loss than the one used to compute the gradients. Even more
surprisingly, SGD does not even converge in the classical sense: we show that
the most likely trajectories of SGD for deep networks do not behave like
Brownian motion around critical points. Instead, they resemble closed loops
with deterministic components. We prove that such "out-of-equilibrium" behavior
is a consequence of highly non-isotropic gradient noise in SGD; the covariance
matrix of mini-batch gradients for deep networks has a rank as small as 1% of
its dimension. We provide extensive empirical validation of these claims,
proven in the appendix.
An Accurate and Real-time Self-blast Glass Insulator Location Method Based On Faster R-CNN and U-net with Aerial Images
Authors: Zenan Ling, Robert C. Qiu, Zhijian Jin, Yuhang Zhang, Xing He, Haichun Liu, Lei Chu
Date: 2018-01-16 00:00:00
Tasks: General Classification, object-detection, Object Detection

The location of broken insulators in aerial images is a challenging task.
This paper, focusing on the self-blast glass insulator, proposes a deep
learning solution. We address the broken insulators location problem as a low
signal-noise-ratio image location framework with two modules: 1) object
detection based on Fast R-CNN, and 2) classification of pixels based on U-net.
A diverse aerial image set of some grid in China is tested to validated the
proposed approach. Furthermore, a comparison is made among different methods
and the result shows that our approach is accurate and real-time.
Image denoising and restoration with CNN-LSTM Encoder Decoder with Direct Attention
Authors: Kazi Nazmul Haque, Mohammad Abu Yousuf, Rajib Rana
Date: 2018-01-16 00:00:00
Tasks: Decoder, Denoising, Image Denoising

Image denoising is always a challenging task in the field of computer vision
and image processing. In this paper, we have proposed an encoder-decoder model
with direct attention, which is capable of denoising and reconstruct highly
corrupted images. Our model consists of an encoder and a decoder, where the
encoder is a convolutional neural network and decoder is a multilayer Long
Short-Term memory network. In the proposed model, the encoder reads an image
and catches the abstraction of that image in a vector, where decoder takes that
vector as well as the corrupted image to reconstruct a clean image. We have
trained our model on MNIST handwritten digit database after making lower half
of every image as black as well as adding noise top of that. After a massive
destruction of the images where it is hard for a human to understand the
content of those images, our model can retrieve that image with minimal error.
Our proposed model has been compared with convolutional encoder-decoder, where
our model has performed better at generating missing part of the images than
convolutional autoencoder.
Understanding the Disharmony between Dropout and Batch Normalization by Variance Shift
Authors: Xiang Li, Shuo Chen, Xiaolin Hu, Jian Yang
Date: 2018-01-16 00:00:00
Proceeding: CVPR 2019 6

This paper first answers the question "why do the two most powerful
techniques Dropout and Batch Normalization (BN) often lead to a worse
performance when they are combined together?" in both theoretical and
statistical aspects. Theoretically, we find that Dropout would shift the
variance of a specific neural unit when we transfer the state of that network
from train to test. However, BN would maintain its statistical variance, which
is accumulated from the entire learning procedure, in the test phase. The
inconsistency of that variance (we name this scheme as "variance shift") causes
the unstable numerical behavior in inference that leads to more erroneous
predictions finally, when applying Dropout before BN. Thorough experiments on
DenseNet, ResNet, ResNeXt and Wide ResNet confirm our findings. According to
the uncovered mechanism, we next explore several strategies that modifies
Dropout and try to overcome the limitations of their combination by avoiding
the variance shift risks.
Localization-Aware Active Learning for Object Detection
Authors: Chieh-Chi Kao, Teng-Yok Lee, Pradeep Sen, Ming-Yu Liu
Date: 2018-01-16 00:00:00
Tasks: Active Learning, Classification, General Classification, image-classification, Image Classification, Informativeness, Object, object-detection, Object Detection, Region Proposal

Active learning - a class of algorithms that iteratively searches for the
most informative samples to include in a training dataset - has been shown to
be effective at annotating data for image classification. However, the use of
active learning for object detection is still largely unexplored as determining
informativeness of an object-location hypothesis is more difficult. In this
paper, we address this issue and present two metrics for measuring the
informativeness of an object hypothesis, which allow us to leverage active
learning to reduce the amount of annotated data needed to achieve a target
object detection performance. Our first metric measures 'localization
tightness' of an object hypothesis, which is based on the overlapping ratio
between the region proposal and the final prediction. Our second metric
measures 'localization stability' of an object hypothesis, which is based on
the variation of predicted object locations when input images are corrupted by
noise. Our experimental results show that by augmenting a conventional
active-learning algorithm designed for classification with the proposed
metrics, the amount of labeled training data required can be reduced up to 25%.
Moreover, on PASCAL 2007 and 2012 datasets our localization-stability method
has an average relative improvement of 96.5% and 81.9% over the baseline method
using classification only.
Variational Recurrent Neural Machine Translation
Authors: Jinsong Su, Shan Wu, Deyi Xiong, Yaojie Lu, Xianpei Han, Biao Zhang
Date: 2018-01-16 00:00:00
Tasks: Decoder, Machine Translation, NMT, Sentence, Translation

Partially inspired by successful applications of variational recurrent neural
networks, we propose a novel variational recurrent neural machine translation
(VRNMT) model in this paper. Different from the variational NMT, VRNMT
introduces a series of latent random variables to model the translation
procedure of a sentence in a generative way, instead of a single latent
variable. Specifically, the latent random variables are included into the
hidden states of the NMT decoder with elements from the variational
autoencoder. In this way, these variables are recurrently generated, which
enables them to further capture strong and complex dependencies among the
output translations at different timesteps. In order to deal with the
challenges in performing efficient posterior inference and large-scale training
during the incorporation of latent variables, we build a neural posterior
approximator, and equip it with a reparameterization technique to estimate the
variational lower bound. Experiments on Chinese-English and English-German
translation tasks demonstrate that the proposed model achieves significant
improvements over both the conventional and variational NMT models.
Reblur2Deblur: Deblurring Videos via Self-Supervised Learning
Authors: Huaijin Chen, Jinwei Gu, Orazio Gallo, Ming-Yu Liu, Ashok Veeraraghavan, Jan Kautz
Date: 2018-01-16 00:00:00
Tasks: Deblurring, Optical Flow Estimation, Self-Supervised Learning

Motion blur is a fundamental problem in computer vision as it impacts image
quality and hinders inference. Traditional deblurring algorithms leverage the
physics of the image formation model and use hand-crafted priors: they usually
produce results that better reflect the underlying scene, but present
artifacts. Recent learning-based methods implicitly extract the distribution of
natural images directly from the data and use it to synthesize plausible
images. Their results are impressive, but they are not always faithful to the
content of the latent image. We present an approach that bridges the two. Our
method fine-tunes existing deblurring neural networks in a self-supervised
fashion by enforcing that the output, when blurred based on the optical flow
between subsequent frames, matches the input blurry image. We show that our
method significantly improves the performance of existing methods on several
datasets both visually and in terms of image quality metrics. The supplementary
material is https://goo.gl/nYPjEQ