pwc-archive/papers-with-abstracts huggingface dataset:
Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentation
Authors: Yunchao Wei, Huaxin Xiao, Honghui Shi, Zequn Jie, Jiashi Feng, Thomas S. Huang
Date: 2018-05-11 00:00:00
Proceeding: CVPR 2018
Tasks: Object, Object Localization, Segmentation, Semantic Segmentation, Semi-Supervised Semantic Segmentation, Weakly supervised segmentation

Despite the remarkable progress, weakly supervised segmentation approaches
are still inferior to their fully supervised counterparts. We obverse the
performance gap mainly comes from their limitation on learning to produce
high-quality dense object localization maps from image-level supervision. To
mitigate such a gap, we revisit the dilated convolution [1] and reveal how it
can be utilized in a novel way to effectively overcome this critical limitation
of weakly supervised segmentation approaches. Specifically, we find that
varying dilation rates can effectively enlarge the receptive fields of
convolutional kernels and more importantly transfer the surrounding
discriminative information to non-discriminative object regions, promoting the
emergence of these regions in the object localization maps. Then, we design a
generic classification network equipped with convolutional blocks of different
dilated rates. It can produce dense and reliable object localization maps and
effectively benefit both weakly- and semi- supervised semantic segmentation.
Despite the apparent simplicity, our proposed approach obtains superior
performance over state-of-the-arts. In particular, it achieves 60.8% and 67.6%
mIoU scores on Pascal VOC 2012 test set in weakly- (only image-level labels are
available) and semi- (1,464 segmentation masks are available) supervised
settings, which are the new state-of-the-arts.
Multi-region segmentation of bladder cancer structures in MRI with progressive dilated convolutional networks
Authors: Jose Dolz, Xiaopan Xu, Jerome Rony, Jing Yuan, Yang Liu, Eric Granger, Christian Desrosiers, Xi Zhang, Ismail Ben Ayed, Hongbing Lu
Date: 2018-05-28 00:00:00
Tasks: Prognosis, Segmentation

Precise segmentation of bladder walls and tumor regions is an essential step
towards non-invasive identification of tumor stage and grade, which is critical
for treatment decision and prognosis of patients with bladder cancer (BC).
However, the automatic delineation of bladder walls and tumor in magnetic
resonance images (MRI) is a challenging task, due to important bladder shape
variations, strong intensity inhomogeneity in urine and very high variability
across population, particularly on tumors appearance. To tackle these issues,
we propose to use a deep fully convolutional neural network. The proposed
network includes dilated convolutions to increase the receptive field without
incurring extra cost nor degrading its performance. Furthermore, we introduce
progressive dilations in each convolutional block, thereby enabling extensive
receptive fields without the need for large dilation rates. The proposed
network is evaluated on 3.0T T2-weighted MRI scans from 60 pathologically
confirmed patients with BC. Experiments shows the proposed model to achieve
high accuracy, with a mean Dice similarity coefficient of 0.98, 0.84 and 0.69
for inner wall, outer wall and tumor region, respectively. These results
represent a very good agreement with reference contours and an increase in
performance compared to existing methods. In addition, inference times are less
than a second for a whole 3D volume, which is between 2-3 orders of magnitude
faster than related state-of-the-art methods for this application. We showed
that a CNN can yield precise segmentation of bladder walls and tumors in
bladder cancer patients on MRI. The whole segmentation process is
fully-automatic and yields results in very good agreement with the reference
standard, demonstrating the viability of deep learning models for the automatic
multi-region segmentation of bladder cancer MRI images.
Discriminator Feature-based Inference by Recycling the Discriminator of GANs
Authors: Duhyeon Bang, Seoungyoon Kang, Hyunjung Shim
Date: 2018-05-28 00:00:00

Generative adversarial networks (GANs)successfully generate high quality data by learning amapping from a latent vector to the data. Various studies assert that the latent space of a GAN is semanticallymeaningful and can be utilized for advanced data analysis and manipulation. To analyze the real data in thelatent space of a GAN, it is necessary to build an inference mapping from the data to the latent vector. Thispaper proposes an effective algorithm to accurately infer the latent vector by utilizing GAN discriminator features. Our primary goal is to increase inference mappingaccuracy with minimal training overhead. Furthermore,using the proposed algorithm, we suggest a conditionalimage generation algorithm, namely a spatially conditioned GAN. Extensive evaluations confirmed that theproposed inference algorithm achieved more semantically accurate inference mapping than existing methodsand can be successfully applied to advanced conditionalimage generation tasks.
Reinforcement and Imitation Learning for Diverse Visuomotor Skills
Authors: Yuke Zhu, Ziyu Wang, Josh Merel, Andrei Rusu, Tom Erez, Serkan Cabi, Saran Tunyasuvunakool, János Kramár, Raia Hadsell, Nando de Freitas, Nicolas Heess
Date: 2018-02-26 00:00:00
Proceeding: ICLR 2018 1
Tasks: Deep Reinforcement Learning, Imitation Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose a model-free deep reinforcement learning method that leverages a
small amount of demonstration data to assist a reinforcement learning agent. We
apply this approach to robotic manipulation tasks and train end-to-end
visuomotor policies that map directly from RGB camera inputs to joint
velocities. We demonstrate that our approach can solve a wide variety of
visuomotor tasks, for which engineering a scripted controller would be
laborious. In experiments, our reinforcement and imitation agent achieves
significantly better performances than agents trained with reinforcement
learning or imitation learning alone. We also illustrate that these policies,
trained with large visual and dynamics variations, can achieve preliminary
successes in zero-shot sim2real transfer. A brief visual description of this
work can be viewed in https://youtu.be/EDl8SQUNjj0
Synergistic Reconstruction and Synthesis via Generative Adversarial Networks for Accelerated Multi-Contrast MRI
Authors: Salman Ul Hassan Dar, Mahmut Yurt, Mohammad Shahdloo, Muhammed Emrullah Ildız, Tolga Çukur
Date: 2018-05-27 00:00:00
Tasks: Anatomy

Multi-contrast MRI acquisitions of an anatomy enrich the magnitude of
information available for diagnosis. Yet, excessive scan times associated with
additional contrasts may be a limiting factor. Two mainstream approaches for
enhanced scan efficiency are reconstruction of undersampled acquisitions and
synthesis of missing acquisitions. In reconstruction, performance decreases
towards higher acceleration factors with diminished sampling density
particularly at high-spatial-frequencies. In synthesis, the absence of data
samples from the target contrast can lead to artefactual sensitivity or
insensitivity to image features. Here we propose a new approach for synergistic
reconstruction-synthesis of multi-contrast MRI based on conditional generative
adversarial networks. The proposed method preserves high-frequency details of
the target contrast by relying on the shared high-frequency information
available from the source contrast, and prevents feature leakage or loss by
relying on the undersampled acquisitions of the target contrast. Demonstrations
on brain MRI datasets from healthy subjects and patients indicate the superior
performance of the proposed method compared to previous state-of-the-art. The
proposed method can help improve the quality and scan efficiency of
multi-contrast MRI exams.
Exponential convergence rates for Batch Normalization: The power of length-direction decoupling in non-convex optimization
Authors: Jonas Kohler, Hadi Daneshmand, Aurelien Lucchi, Ming Zhou, Klaus Neymeyr, Thomas Hofmann
Date: 2018-05-27 00:00:00

Normalization techniques such as Batch Normalization have been applied
successfully for training deep neural networks. Yet, despite its apparent
empirical benefits, the reasons behind the success of Batch Normalization are
mostly hypothetical. We here aim to provide a more thorough theoretical
understanding from a classical optimization perspective. Our main contribution
towards this goal is the identification of various problem instances in the
realm of machine learning where % -- under certain assumptions-- Batch
Normalization can provably accelerate optimization. We argue that this
acceleration is due to the fact that Batch Normalization splits the
optimization task into optimizing length and direction of the parameters
separately. This allows gradient-based methods to leverage a favourable global
structure in the loss landscape that we prove to exist in Learning Halfspace
problems and neural network training with Gaussian inputs. We thereby turn
Batch Normalization from an effective practical heuristic into a provably
converging algorithm for these settings. Furthermore, we substantiate our
analysis with empirical evidence that suggests the validity of our theoretical
results in a broader context.
Strategyproof Linear Regression in High Dimensions
Authors: Yiling Chen, Chara Podimata, Ariel D. Procaccia, Nisarg Shah
Date: 2018-05-27 00:00:00
Tasks: regression, Vocal Bursts Intensity Prediction

This paper is part of an emerging line of work at the intersection of machine
learning and mechanism design, which aims to avoid noise in training data by
correctly aligning the incentives of data sources. Specifically, we focus on
the ubiquitous problem of linear regression, where strategyproof mechanisms
have previously been identified in two dimensions. In our setting, agents have
single-peaked preferences and can manipulate only their response variables. Our
main contribution is the discovery of a family of group strategyproof linear
regression mechanisms in any number of dimensions, which we call generalized
resistant hyperplane mechanisms. The game-theoretic properties of these
mechanisms -- and, in fact, their very existence -- are established through a
connection to a discrete version of the Ham Sandwich Theorem.
Compact and Computationally Efficient Representation of Deep Neural Networks
Authors: Simon Wiedemann, Klaus-Robert Müller, Wojciech Samek
Date: 2018-05-27 00:00:00

At the core of any inference procedure in deep neural networks are dot
product operations, which are the component that require the highest
computational resources. A common approach to reduce the cost of inference is
to reduce its memory complexity by lowering the entropy of the weight matrices
of the neural network, e.g., by pruning and quantizing their elements. However,
the quantized weight matrices are then usually represented either by a dense or
sparse matrix storage format, whose associated dot product complexity is not
bounded by the entropy of the matrix. This means that the associated inference
complexity ultimately depends on the implicit statistical assumptions that
these matrix representations make about the weight distribution, which can be
in many cases suboptimal. In this paper we address this issue and present new
efficient representations for matrices with low entropy statistics. These new
matrix formats have the novel property that their memory and algorithmic
complexity are implicitly bounded by the entropy of the matrix, consequently
implying that they are guaranteed to become more efficient as the entropy of
the matrix is being reduced. In our experiments we show that performing the dot
product under these new matrix formats can indeed be more energy and time
efficient under practically relevant assumptions. For instance, we are able to
attain up to x42 compression ratios, x5 speed ups and x90 energy savings when
we convert in a lossless manner the weight matrices of state-of-the-art
networks such as AlexNet, VGG-16, ResNet152 and DenseNet into the new matrix
formats and benchmark their respective dot product operation.
Identifying Object States in Cooking-Related Images
Authors: Ahmad Babaeian Jelodar, Md Sirajus Salekin, Yu Sun
Date: 2018-05-17 00:00:00
Tasks: Object, Object Recognition, Task Planning

Understanding object states is as important as object recognition for robotic
task planning and manipulation. To our knowledge, this paper explicitly
introduces and addresses the state identification problem in cooking related
images for the first time. In this paper, objects and ingredients in cooking
videos are explored and the most frequent objects are analyzed. Eleven states
from the most frequent cooking objects are examined and a dataset of images
containing those objects and their states is created. As a solution to the
state identification problem, a Resnet based deep model is proposed. The model
is initialized with Imagenet weights and trained on the dataset of eleven
classes. The trained state identification model is evaluated on a subset of the
Imagenet dataset and state labels are provided using a combination of the model
with manual checking. Moreover, an individual model is fine-tuned for each
object in the dataset using the weights from the initially trained model and
object-specific images, where significant improvement is demonstrated.
GAN Q-learning
Authors: Thang Doan, Bogdan Mazoure, Clare Lyle
Date: 2018-05-13 00:00:00
Tasks: Distributional Reinforcement Learning, OpenAI Gym, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Distributional reinforcement learning (distributional RL) has seen empirical
success in complex Markov Decision Processes (MDPs) in the setting of nonlinear
function approximation. However, there are many different ways in which one can
leverage the distributional approach to reinforcement learning. In this paper,
we propose GAN Q-learning, a novel distributional RL method based on generative
adversarial networks (GANs) and analyze its performance in simple tabular
environments, as well as OpenAI Gym. We empirically show that our algorithm
leverages the flexibility and blackbox approach of deep learning models while
providing a viable alternative to traditional methods.
EndNet: Sparse AutoEncoder Network for Endmember Extraction and Hyperspectral Unmixing
Authors: Savas Ozkan, Berk Kaya, Gozde Bozdagi Akar
Date: 2017-08-06 00:00:00
Tasks: Hyperspectral Unmixing

Data acquired from multi-channel sensors is a highly valuable asset to
interpret the environment for a variety of remote sensing applications.
However, low spatial resolution is a critical limitation for previous sensors
and the constituent materials of a scene can be mixed in different fractions
due to their spatial interactions. Spectral unmixing is a technique that allows
us to obtain the material spectral signatures and their fractions from
hyperspectral data. In this paper, we propose a novel endmember extraction and
hyperspectral unmixing scheme, so called \textit{EndNet}, that is based on a
two-staged autoencoder network. This well-known structure is completely
enhanced and restructured by introducing additional layers and a projection
metric (i.e., spectral angle distance (SAD) instead of inner product) to
achieve an optimum solution. Moreover, we present a novel loss function that is
composed of a Kullback-Leibler divergence term with SAD similarity and
additional penalty terms to improve the sparsity of the estimates. These
modifications enable us to set the common properties of endmembers such as
non-linearity and sparsity for autoencoder networks. Lastly, due to the
stochastic-gradient based approach, the method is scalable for large-scale data
and it can be accelerated on Graphical Processing Units (GPUs). To demonstrate
the superiority of our proposed method, we conduct extensive experiments on
several well-known datasets. The results confirm that the proposed method
considerably improves the performance compared to the state-of-the-art
techniques in literature.
Inference Suboptimality in Variational Autoencoders
Authors: Chris Cremer, Xuechen Li, David Duvenaud
Date: 2018-01-10 00:00:00
Proceeding: ICML 2018 7

Amortized inference allows latent-variable models trained via variational
learning to scale to large datasets. The quality of approximate inference is
determined by two factors: a) the capacity of the variational distribution to
match the true posterior and b) the ability of the recognition network to
produce good variational parameters for each datapoint. We examine approximate
inference in variational autoencoders in terms of these factors. We find that
divergence from the true posterior is often due to imperfect recognition
networks, rather than the limited complexity of the approximating distribution.
We show that this is due partly to the generator learning to accommodate the
choice of approximation. Furthermore, we show that the parameters used to
increase the expressiveness of the approximation play a role in generalizing
inference rather than simply improving the complexity of the approximation.
Adversarial Deformation Regularization for Training Image Registration Neural Networks
Authors: Yipeng Hu, Eli Gibson, Nooshin Ghavami, Ester Bonmati, Caroline M. Moore, Mark Emberton, Tom Vercauteren, J. Alison Noble, Dean C. Barratt
Date: 2018-05-27 00:00:00
Tasks: Image Registration

We describe an adversarial learning approach to constrain convolutional
neural network training for image registration, replacing heuristic smoothness
measures of displacement fields often used in these tasks. Using
minimally-invasive prostate cancer intervention as an example application, we
demonstrate the feasibility of utilizing biomechanical simulations to
regularize a weakly-supervised anatomical-label-driven registration network for
aligning pre-procedural magnetic resonance (MR) and 3D intra-procedural
transrectal ultrasound (TRUS) images. A discriminator network is optimized to
distinguish the registration-predicted displacement fields from the motion data
simulated by finite element analysis. During training, the registration network
simultaneously aims to maximize similarity between anatomical labels that
drives image alignment and to minimize an adversarial generator loss that
measures divergence between the predicted- and simulated deformation. The
end-to-end trained network enables efficient and fully-automated registration
that only requires an MR and TRUS image pair as input, without anatomical
labels or simulated data during inference. 108 pairs of labelled MR and TRUS
images from 76 prostate cancer patients and 71,500 nonlinear finite-element
simulations from 143 different patients were used for this study. We show that,
with only gland segmentation as training labels, the proposed method can help
predict physically plausible deformation without any other smoothness penalty.
Based on cross-validation experiments using 834 pairs of independent validation
landmarks, the proposed adversarial-regularized registration achieved a target
registration error of 6.3 mm that is significantly lower than those from
several other regularization methods.
Fingerprint Policy Optimisation for Robust Reinforcement Learning
Authors: Supratik Paul, Michael A. Osborne, Shimon Whiteson
Date: 2018-05-27 00:00:00
Tasks: Bayesian Optimisation, Continuous Control, Policy Gradient Methods, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Policy gradient methods ignore the potential value of adjusting environment variables: unobservable state features that are randomly determined by the environment in a physical setting, but are controllable in a simulator. This can lead to slow learning, or convergence to suboptimal policies, if the environment variable has a large impact on the transition dynamics. In this paper, we present fingerprint policy optimisation (FPO), which finds a policy that is optimal in expectation across the distribution of environment variables. The central idea is to use Bayesian optimisation (BO) to actively select the distribution of the environment variable that maximises the improvement generated by each iteration of the policy gradient method. To make this BO practical, we contribute two easy-to-compute low-dimensional fingerprints of the current policy. Our experiments show that FPO can efficiently learn policies that are robust to significant rare events, which are unlikely to be observable under random sampling, but are key to learning good policies.
Graph sketching-based Space-efficient Data Clustering
Authors: Anne Morvan, Krzysztof Choromanski, Cédric Gouy-Pailler, Jamal Atif
Date: 2017-03-07 00:00:00
Tasks: Clustering

In this paper, we address the problem of recovering arbitrary-shaped data
clusters from datasets while facing \emph{high space constraints}, as this is
for instance the case in many real-world applications when analysis algorithms
are directly deployed on resources-limited mobile devices collecting the data.
We present DBMSTClu a new space-efficient density-based \emph{non-parametric}
method working on a Minimum Spanning Tree (MST) recovered from a limited number
of linear measurements i.e. a \emph{sketched} version of the dissimilarity
graph $\mathcal{G}$ between the $N$ objects to cluster. Unlike $k$-means,
$k$-medians or $k$-medoids algorithms, it does not fail at distinguishing
clusters with particular forms thanks to the property of the MST for expressing
the underlying structure of a graph. No input parameter is needed contrarily to
DBSCAN or the Spectral Clustering method. An approximate MST is retrieved by
following the dynamic \emph{semi-streaming} model in handling the dissimilarity
graph $\mathcal{G}$ as a stream of edge weight updates which is sketched in one
pass over the data into a compact structure requiring $O(N
\operatorname{polylog}(N))$ space, far better than the theoretical memory cost
$O(N^2)$ of $\mathcal{G}$. The recovered approximate MST $\mathcal{T}$ as
input, DBMSTClu then successfully detects the right number of nonconvex
clusters by performing relevant cuts on $\mathcal{T}$ in a time linear in $N$.
We provide theoretical guarantees on the quality of the clustering partition
and also demonstrate its advantage over the existing state-of-the-art on
several datasets.
Generalization Challenges for Neural Architectures in Audio Source Separation
Authors: Shariq Mobin, Brian Cheung, Bruno Olshausen
Date: 2018-03-23 00:00:00
Tasks: Audio Source Separation

Recent work has shown that recurrent neural networks can be trained to
separate individual speakers in a sound mixture with high fidelity. Here we
explore convolutional neural network models as an alternative and show that
they achieve state-of-the-art results with an order of magnitude fewer
parameters. We also characterize and compare the robustness and ability of
these different approaches to generalize under three different test conditions:
longer time sequences, the addition of intermittent noise, and different
datasets not seen during training. For the last condition, we create a new
dataset, RealTalkLibri, to test source separation in real-world environments.
We show that the acoustics of the environment have significant impact on the
structure of the waveform and the overall performance of neural network models,
with the convolutional model showing superior ability to generalize to new
environments. The code for our study is available at
https://github.com/ShariqM/source_separation.