pwc-archive/papers-with-abstracts huggingface dataset:
Training Deep Learning Based Denoisers without Ground Truth Data
Authors: Shakarim Soltanayev, Se Young Chun
Date: 2018-03-04 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Deep Learning, Image Denoising

Recently developed deep-learning-based denoisers often outperform state-of-the-art conventional denoisers such as the BM3D. They are typically trained to minimize the mean squared error (MSE) between the output image of a deep neural network (DNN) and a ground truth image. Thus, it is important for deep-learning-based denoisers to use high quality noiseless ground truth data for high performance. However, it is often challenging or even infeasible to obtain noiseless images in some applications. Here, we propose a method based on Stein's unbiased risk estimator (SURE) for training DNN denoisers based only on the use of noisy images in the training data with Gaussian noise. We demonstrate that our SURE-based method, without the use of ground truth data, is able to train DNN denoisers to yield performances close to those networks trained with ground truth for both grayscale and color images. We also propose a SURE-based refining method with a noisy test image for further performance improvement. Our quick refining method outperformed conventional BM3D, deep image prior, and often the networks trained with ground truth. Potential extension of our SURE-based methods to Poisson noise model was also investigated.
A Transition-based Algorithm for Unrestricted AMR Parsing
Authors: David Vilares, Carlos Gómez-Rodríguez
Date: 2018-05-23 00:00:00
Proceeding: NAACL 2018 6
Tasks: AMR Parsing

Non-projective parsing can be useful to handle cycles and reentrancy in AMR
graphs. We explore this idea and introduce a greedy left-to-right
non-projective transition-based parser. At each parsing configuration, an
oracle decides whether to create a concept or whether to connect a pair of
existing concepts. The algorithm handles reentrancy and arbitrary cycles
natively, i.e. within the transition system itself. The model is evaluated on
the LDC2015E86 corpus, obtaining results close to the state of the art,
including a Smatch of 64%, and showing good behavior on reentrant edges.
The Description Length of Deep Learning Models
Authors: Léonard Blier, Yann Ollivier
Date: 2018-02-20 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Deep Learning

Solomonoff's general theory of inference and the Minimum Description Length
principle formalize Occam's razor, and hold that a good model of data is a
model that is good at losslessly compressing the data, including the cost of
describing the model itself. Deep neural networks might seem to go against this
principle given the large number of parameters to be encoded.
  We demonstrate experimentally the ability of deep neural networks to compress
the training data even when accounting for parameter encoding. The compression
viewpoint originally motivated the use of variational methods in neural
networks. Unexpectedly, we found that these variational methods provide
surprisingly poor compression bounds, despite being explicitly built to
minimize such bounds. This might explain the relatively poor practical
performance of variational methods in deep learning. On the other hand, simple
incremental encoding methods yield excellent compression values on deep
networks, vindicating Solomonoff's approach.
GPU Accelerated Cascade Hashing Image Matching for Large Scale 3D Reconstruction
Authors: Tao Xu, Kun Sun, Wenbing Tao
Date: 2018-05-23 00:00:00
Tasks: 3D Reconstruction, CPU, GPU

Image feature point matching is a key step in Structure from Motion(SFM).
However, it is becoming more and more time consuming because the number of
images is getting larger and larger. In this paper, we proposed a GPU
accelerated image matching method with improved Cascade Hashing. Firstly, we
propose a Disk-Memory-GPU data exchange strategy and optimize the load order of
data, so that the proposed method can deal with big data. Next, we parallelize
the Cascade Hashing method on GPU. An improved parallel reduction and an
improved parallel hashing ranking are proposed to fulfill this task. Finally,
extensive experiments show that our image matching is about 20 times faster
than SiftGPU on the same graphics card, nearly 100 times faster than the CPU
CasHash method and hundreds of times faster than the CPU Kd-Tree based matching
method. Further more, we introduce the epipolar constraint to the proposed
method, and use the epipolar geometry to guide the feature matching procedure,
which further reduces the matching cost.
Data-Efficient Hierarchical Reinforcement Learning
Authors: Ofir Nachum, Shixiang Gu, Honglak Lee, Sergey Levine
Date: 2018-05-21 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Hierarchical reinforcement learning (HRL) is a promising approach to extend
traditional reinforcement learning (RL) methods to solve more complex tasks.
Yet, the majority of current HRL methods require careful task-specific design
and on-policy training, making them difficult to apply in real-world scenarios.
In this paper, we study how we can develop HRL algorithms that are general, in
that they do not make onerous additional assumptions beyond standard RL
algorithms, and efficient, in the sense that they can be used with modest
numbers of interaction samples, making them suitable for real-world problems
such as robotic control. For generality, we develop a scheme where lower-level
controllers are supervised with goals that are learned and proposed
automatically by the higher-level controllers. To address efficiency, we
propose to use off-policy experience for both higher and lower-level training.
This poses a considerable challenge, since changes to the lower-level behaviors
change the action space for the higher-level policy, and we introduce an
off-policy correction to remedy this challenge. This allows us to take
advantage of recent advances in off-policy model-free RL to learn both higher-
and lower-level policies using substantially fewer environment interactions
than on-policy algorithms. We term the resulting HRL agent HIRO and find that
it is generally applicable and highly sample-efficient. Our experiments show
that HIRO can be used to learn highly complex behaviors for simulated robots,
such as pushing objects and utilizing them to reach target locations, learning
from only a few million samples, equivalent to a few days of real-time
interaction. In comparisons with a number of prior HRL methods, we find that
our approach substantially outperforms previous state-of-the-art techniques.
$\mathcal{G}$-SGD: Optimizing ReLU Neural Networks in its Positively Scale-Invariant Space
Authors: Qi Meng, Shuxin Zheng, Huishuai Zhang, Wei Chen, Zhi-Ming Ma, Tie-Yan Liu
Date: 2018-02-11 00:00:00

It is well known that neural networks with rectified linear units (ReLU) activation functions are positively scale-invariant. Conventional algorithms like stochastic gradient descent optimize the neural networks in the vector space of weights, which is, however, not positively scale-invariant. This mismatch may lead to problems during the optimization process. Then, a natural question is: \emph{can we construct a new vector space that is positively scale-invariant and sufficient to represent ReLU neural networks so as to better facilitate the optimization process }? In this paper, we provide our positive answer to this question. First, we conduct a formal study on the positive scaling operators which forms a transformation group, denoted as $\mathcal{G}$. We show that the value of a path (i.e. the product of the weights along the path) in the neural network is invariant to positive scaling and prove that the value vector of all the paths is sufficient to represent the neural networks under mild conditions. Second, we show that one can identify some basis paths out of all the paths and prove that the linear span of their value vectors (denoted as $\mathcal{G}$-space) is an invariant space with lower dimension under the positive scaling group. Finally, we design stochastic gradient descent algorithm in $\mathcal{G}$-space (abbreviated as $\mathcal{G}$-SGD) to optimize the value vector of the basis paths of neural networks with little extra cost by leveraging back-propagation. Our experiments show that $\mathcal{G}$-SGD significantly outperforms the conventional SGD algorithm in optimizing ReLU networks on benchmark datasets.
Deep Multi-Spectral Registration Using Invariant Descriptor Learning
Authors: Nati Ofir, Shai Silberstein, Hila Levi, Dani Rozenbaum, Yosi Keller, Sharon Duvdevani Bar
Date: 2018-01-16 00:00:00

In this paper, we introduce a novel deep-learning method to align
cross-spectral images. Our approach relies on a learned descriptor which is
invariant to different spectra. Multi-modal images of the same scene capture
different signals and therefore their registration is challenging and it is not
solved by classic approaches. To that end, we developed a feature-based
approach that solves the visible (VIS) to Near-Infra-Red (NIR) registration
problem. Our algorithm detects corners by Harris and matches them by a
patch-metric learned on top of CIFAR-10 network descriptor. As our experiments
demonstrate we achieve a high-quality alignment of cross-spectral images with a
sub-pixel accuracy. Comparing to other existing methods, our approach is more
accurate in the task of VIS to NIR registration.
Self-Attention-Based Message-Relevant Response Generation for Neural Conversation Model
Authors: Jonggu Kim, Doyeon Kong, Jong-Hyeok Lee
Date: 2018-05-23 00:00:00
Tasks: Dialogue Generation, Response Generation

Using a sequence-to-sequence framework, many neural conversation models for
chit-chat succeed in naturalness of the response. Nevertheless, the neural
conversation models tend to give generic responses which are not specific to
given messages, and it still remains as a challenge. To alleviate the tendency,
we propose a method to promote message-relevant and diverse responses for
neural conversation model by using self-attention, which is time-efficient as
well as effective. Furthermore, we present an investigation of why and how
effective self-attention is in deep comparison with the standard dialogue
generation. The experiment results show that the proposed method improves the
standard dialogue generation in various evaluation metrics.
RGB-T Object Tracking:Benchmark and Baseline
Authors: Chenglong Li, Xinyan Liang, Yijuan Lu, Nan Zhao, Jin Tang
Date: 2018-05-23 00:00:00
Tasks: 8k, Object, Object Tracking, Rgb-T Tracking

RGB-Thermal (RGB-T) object tracking receives more and more attention due to
the strongly complementary benefits of thermal information to visible data.
However, RGB-T research is limited by lacking a comprehensive evaluation
platform. In this paper, we propose a large-scale video benchmark dataset for
RGB-T tracking.It has three major advantages over existing ones: 1) Its size is
sufficiently large for large-scale performance evaluation (total frame number:
234K, maximum frame per sequence: 8K). 2) The alignment between RGB-T sequence
pairs is highly accurate, which does not need pre- or post-processing. 3) The
occlusion levels are annotated for occlusion-sensitive performance analysis of
different tracking algorithms.Moreover, we propose a novel graph-based approach
to learn a robust object representation for RGB-T tracking. In particular, the
tracked object is represented with a graph with image patches as nodes. This
graph including graph structure, node weights and edge weights is dynamically
learned in a unified ADMM (alternating direction method of multipliers)-based
optimization framework, in which the modality weights are also incorporated for
adaptive fusion of multiple source data.Extensive experiments on the
large-scale dataset are executed to demonstrate the effectiveness of the
proposed tracker against other state-of-the-art tracking methods. We also
provide new insights and potential research directions to the field of RGB-T
object tracking.
Single-View Food Portion Estimation: Learning Image-to-Energy Mappings Using Generative Adversarial Networks
Authors: Shaobo Fang, Zeman Shao, Runyu Mao, Chichen Fu, Deborah A. Kerr, Carol J. Boushey, Edward J. Delp, Fengqing Zhu
Date: 2018-02-27 00:00:00

Due to the growing concern of chronic diseases and other health problems
related to diet, there is a need to develop accurate methods to estimate an
individual's food and energy intake. Measuring accurate dietary intake is an
open research problem. In particular, accurate food portion estimation is
challenging since the process of food preparation and consumption impose large
variations on food shapes and appearances. In this paper, we present a food
portion estimation method to estimate food energy (kilocalories) from food
images using Generative Adversarial Networks (GAN). We introduce the concept of
an "energy distribution" for each food image. To train the GAN, we design a
food image dataset based on ground truth food labels and segmentation masks for
each food image as well as energy information associated with the food image.
Our goal is to learn the mapping of the food image to the food energy. We can
then estimate food energy based on the energy distribution. We show that an
average energy estimation error rate of 10.89% can be obtained by learning the
image-to-energy mapping.
A Unified View of Causal and Non-causal Feature Selection
Authors: Kui Yu, Lin Liu, Jiuyong Li
Date: 2018-02-16 00:00:00
Tasks: Attribute, feature selection

In this paper, we aim to develop a unified view of causal and non-causal
feature selection methods. The unified view will fill in the gap in the
research of the relation between the two types of methods. Based on the
Bayesian network framework and information theory, we first show that causal
and non-causal feature selection methods share the same objective. That is to
find the Markov blanket of a class attribute, the theoretically optimal feature
set for classification. We then examine the assumptions made by causal and
non-causal feature selection methods when searching for the optimal feature
set, and unify the assumptions by mapping them to the restrictions on the
structure of the Bayesian network model of the studied problem. We further
analyze in detail how the structural assumptions lead to the different levels
of approximations employed by the methods in their search, which then result in
the approximations in the feature sets found by the methods with respect to the
optimal feature set. With the unified view, we are able to interpret the output
of non-causal methods from a causal perspective and derive the error bounds of
both types of methods. Finally, we present practical understanding of the
relation between causal and non-causal methods using extensive experiments with
synthetic data and various types of real-word data.
Differentiating the multipoint Expected Improvement for optimal batch design
Authors: Sébastien Marmin, Clément Chevalier, David Ginsbourger
Date: 2015-03-18 00:00:00
Tasks: Bayesian Optimization, Gaussian Processes

This work deals with parallel optimization of expensive objective functions which are modeled as sample realizations of Gaussian processes. The study is formalized as a Bayesian optimization problem, or continuous multi-armed bandit problem, where a batch of q > 0 arms is pulled in parallel at each iteration. Several algorithms have been developed for choosing batches by trading off exploitation and exploration. As of today, the maximum Expected Improvement (EI) and Upper Confidence Bound (UCB) selection rules appear as the most prominent approaches for batch selection. Here, we build upon recent work on the multipoint Expected Improvement criterion, for which an analytic expansion relying on Tallis' formula was recently established. The computational burden of this selection rule being still an issue in application, we derive a closed-form expression for the gradient of the multipoint Expected Improvement, which aims at facilitating its maximization using gradient-based ascent algorithms. Substantial computational savings are shown in application. In addition, our algorithms are tested numerically and compared to state-of-the-art UCB-based batch-sequential algorithms. Combining starting designs relying on UCB with gradient-based EI local optimization finally appears as a sound option for batch design in distributed Gaussian Process optimization.
Particle Filter Networks with Application to Visual Localization
Authors: Peter Karkus, David Hsu, Wee Sun Lee
Date: 2018-05-23 00:00:00
Tasks: Object Tracking, State Estimation, Visual Localization

Particle filtering is a powerful approach to sequential state estimation and
finds application in many domains, including robot localization, object
tracking, etc. To apply particle filtering in practice, a critical challenge is
to construct probabilistic system models, especially for systems with complex
dynamics or rich sensory inputs such as camera images. This paper introduces
the Particle Filter Network (PFnet), which encodes both a system model and a
particle filter algorithm in a single neural network. The PF-net is fully
differentiable and trained end-to-end from data. Instead of learning a generic
system model, it learns a model optimized for the particle filter algorithm. We
apply the PF-net to a visual localization task, in which a robot must localize
in a rich 3-D world, using only a schematic 2-D floor map. In simulation
experiments, PF-net consistently outperforms alternative learning
architectures, as well as a traditional model-based method, under a variety of
sensor inputs. Further, PF-net generalizes well to new, unseen environments.
Do Better ImageNet Models Transfer Better?
Authors: Simon Kornblith, Jonathon Shlens, Quoc V. Le
Date: 2018-05-23 00:00:00
Proceeding: CVPR 2019 6
Tasks: Fine-Grained Image Classification, General Classification, image-classification, Image Classification, Transfer Learning

Transfer learning is a cornerstone of computer vision, yet little work has been done to evaluate the relationship between architecture and transfer. An implicit hypothesis in modern computer vision research is that models that perform better on ImageNet necessarily perform better on other vision tasks. However, this hypothesis has never been systematically tested. Here, we compare the performance of 16 classification networks on 12 image classification datasets. We find that, when networks are used as fixed feature extractors or fine-tuned, there is a strong correlation between ImageNet accuracy and transfer accuracy ($r = 0.99$ and $0.96$, respectively). In the former setting, we find that this relationship is very sensitive to the way in which networks are trained on ImageNet; many common forms of regularization slightly improve ImageNet accuracy but yield penultimate layer features that are much worse for transfer learning. Additionally, we find that, on two small fine-grained image classification datasets, pretraining on ImageNet provides minimal benefits, indicating the learned features from ImageNet do not transfer well to fine-grained tasks. Together, our results show that ImageNet architectures generalize well across datasets, but ImageNet features are less general than previously suggested.
Toward a Thinking Microscope: Deep Learning in Optical Microscopy and Image Reconstruction
Authors: Yair Rivenson, Aydogan Ozcan
Date: 2018-05-23 00:00:00
Tasks: Deep Learning, Image Reconstruction

We discuss recently emerging applications of the state-of-art deep learning
methods on optical microscopy and microscopic image reconstruction, which
enable new transformations among different modes and modalities of microscopic
imaging, driven entirely by image data. We believe that deep learning will
fundamentally change both the hardware and image reconstruction methods used in
optical microscopy in a holistic manner.
Semantic Network Interpretation
Authors: Pei Guo, Ryan Farrell
Date: 2018-05-23 00:00:00
Tasks: Network Interpretation, Sentence

Network interpretation as an effort to reveal the features learned by a network remains largely visualization-based. In this paper, our goal is to tackle semantic network interpretation at both filter and decision level. For filter-level interpretation, we represent the concepts a filter encodes with a probability distribution of visual attributes. The decision-level interpretation is achieved by textual summarization that generates an explanatory sentence containing clues behind a network's decision. A Bayesian inference algorithm is proposed to automatically associate filters and network decisions with visual attributes. Human study confirms that the semantic interpretation is a beneficial alternative or complement to visualization methods. We demonstrate the crucial role that semantic network interpretation can play in understanding a network's failure patterns. More importantly, semantic network interpretation enables a better understanding of the correlation between a model's performance and its distribution metrics like filter selectivity and concept sparseness.
Discovering Blind Spots in Reinforcement Learning
Authors: Ramya Ramakrishnan, Ece Kamar, Debadeepta Dey, Julie Shah, Eric Horvitz
Date: 2018-05-23 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Agents trained in simulation may make errors in the real world due to
mismatches between training and execution environments. These mistakes can be
dangerous and difficult to discover because the agent cannot predict them a
priori. We propose using oracle feedback to learn a predictive model of these
blind spots to reduce costly errors in real-world applications. We focus on
blind spots in reinforcement learning (RL) that occur due to incomplete state
representation: The agent does not have the appropriate features to represent
the true state of the world and thus cannot distinguish among numerous states.
We formalize the problem of discovering blind spots in RL as a noisy supervised
learning problem with class imbalance. We learn models to predict blind spots
in unseen regions of the state space by combining techniques for label
aggregation, calibration, and supervised learning. The models take into
consideration noise emerging from different forms of oracle feedback, including
demonstrations and corrections. We evaluate our approach on two domains and
show that it achieves higher predictive performance than baseline methods, and
that the learned model can be used to selectively query an oracle at execution
time to prevent errors. We also empirically analyze the biases of various
feedback types and how they influence the discovery of blind spots.
3D Human Pose Estimation with Relational Networks
Authors: Sungheon Park, Nojun Kwak
Date: 2018-05-23 00:00:00
Tasks: 3D Human Pose Estimation, 3D Pose Estimation, Pose Estimation

In this paper, we propose a novel 3D human pose estimation algorithm from a
single image based on neural networks. We adopted the structure of the
relational networks in order to capture the relations among different body
parts. In our method, each pair of different body parts generates features, and
the average of the features from all the pairs are used for 3D pose estimation.
In addition, we propose a dropout method that can be used in relational
modules, which inherently imposes robustness to the occlusions. The proposed
network achieves state-of-the-art performance for 3D pose estimation in Human
3.6M dataset, and it effectively produces plausible results even in the
existence of missing joints.