pwc-archive/papers-with-abstracts huggingface dataset:
SPLAT: Semantic Pixel-Level Adaptation Transforms for Detection
Authors: Eric Tzeng, Kaylee Burns, Kate Saenko, Trevor Darrell
Date: 2018-12-03 00:00:00
Tasks: Domain Adaptation, Pseudo Label, Semantic Segmentation

Domain adaptation of visual detectors is a critical challenge, yet existing
methods have overlooked pixel appearance transformations, focusing instead on
bootstrapping and/or domain confusion losses. We propose a Semantic Pixel-Level
Adaptation Transform (SPLAT) approach to detector adaptation that efficiently
generates cross-domain image pairs. Our model uses aligned-pair and/or
pseudo-label losses to adapt an object detector to the target domain, and can
learn transformations with or without densely labeled data in the source (e.g.
semantic segmentation annotations). Without dense labels, as is the case when
only detection labels are available in the source, transformations are learned
using CycleGAN alignment. Otherwise, when dense labels are available we
introduce a more efficient cycle-free method, which exploits pixel-level
semantic labels to condition the training of the transformation network. The
end task is then trained using detection box labels from the source,
potentially including labels inferred on unlabeled source data. We show both
that pixel-level transforms outperform prior approaches to detector domain
adaptation, and that our cycle-free method outperforms prior models for
unconstrained cycle-based learning of generic transformations while running 3.8
times faster. Our combined model improves on prior detection baselines by 12.5
mAP adapting from Sim 10K to Cityscapes, recovering over 50% of the missing
performance between the unadapted baseline and the labeled-target upper bound.
Multi-agent Deep Reinforcement Learning with Extremely Noisy Observations
Authors: Ozsel Kilinc, Giovanni Montana
Date: 2018-12-03 00:00:00
Tasks: Deep Reinforcement Learning, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Multi-agent reinforcement learning systems aim to provide interacting agents
with the ability to collaboratively learn and adapt to the behaviour of other
agents. In many real-world applications, the agents can only acquire a partial
view of the world. Here we consider a setting whereby most agents' observations
are also extremely noisy, hence only weakly correlated to the true state of the
environment. Under these circumstances, learning an optimal policy becomes
particularly challenging, even in the unrealistic case that an agent's policy
can be made conditional upon all other agents' observations. To overcome these
difficulties, we propose a multi-agent deep deterministic policy gradient
algorithm enhanced by a communication medium (MADDPG-M), which implements a
two-level, concurrent learning mechanism. An agent's policy depends on its own
private observations as well as those explicitly shared by others through a
communication medium. At any given point in time, an agent must decide whether
its private observations are sufficiently informative to be shared with others.
However, our environments provide no explicit feedback informing an agent
whether a communication action is beneficial, rather the communication policies
must also be learned through experience concurrently to the main policies. Our
experimental results demonstrate that the algorithm performs well in six highly
non-stationary environments of progressively higher complexity, and offers
substantial performance gains compared to the baselines.
Accelerating Large Scale Knowledge Distillation via Dynamic Importance Sampling
Authors: Minghan Li, Tanli Zuo, Ruicheng Li, Martha White, Wei-Shi Zheng
Date: 2018-12-03 00:00:00
Tasks: Knowledge Distillation, Machine Translation, Person Re-Identification, Translation

Knowledge distillation is an effective technique that transfers knowledge
from a large teacher model to a shallow student. However, just like massive
classification, large scale knowledge distillation also imposes heavy
computational costs on training models of deep neural networks, as the softmax
activations at the last layer involve computing probabilities over numerous
classes. In this work, we apply the idea of importance sampling which is often
used in Neural Machine Translation on large scale knowledge distillation. We
present a method called dynamic importance sampling, where ranked classes are
sampled from a dynamic distribution derived from the interaction between the
teacher and student in full distillation. We highlight the utility of our
proposal prior which helps the student capture the main information in the loss
function. Our approach manages to reduce the computational cost at training
time while maintaining the competitive performance on CIFAR-100 and Market-1501
person re-identification datasets.
The Right (Angled) Perspective: Improving the Understanding of Road Scenes Using Boosted Inverse Perspective Mapping
Authors: Tom Bruls, Horia Porav, Lars Kunze, Paul Newman
Date: 2018-12-03 00:00:00
Tasks: Autonomous Vehicles, Object Tracking, Scene Understanding

Many tasks performed by autonomous vehicles such as road marking detection,
object tracking, and path planning are simpler in bird's-eye view. Hence,
Inverse Perspective Mapping (IPM) is often applied to remove the perspective
effect from a vehicle's front-facing camera and to remap its images into a 2D
domain, resulting in a top-down view. Unfortunately, however, this leads to
unnatural blurring and stretching of objects at further distance, due to the
resolution of the camera, limiting applicability. In this paper, we present an
adversarial learning approach for generating a significantly improved IPM from
a single camera image in real time. The generated bird's-eye-view images
contain sharper features (e.g. road markings) and a more homogeneous
illumination, while (dynamic) objects are automatically removed from the scene,
thus revealing the underlying road layout in an improved fashion. We
demonstrate our framework using real-world data from the Oxford RobotCar
Dataset and show that scene understanding tasks directly benefit from our
boosted IPM approach.
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning
Authors: Milad Nasr, Reza Shokri, Amir Houmansadr
Date: 2018-12-03 00:00:00
Tasks: Deep Learning, Federated Learning

Deep neural networks are susceptible to various inference attacks as they remember information about their training data. We design white-box inference attacks to perform a comprehensive privacy analysis of deep learning models. We measure the privacy leakage through parameters of fully trained models as well as the parameter updates of models during training. We design inference algorithms for both centralized and federated learning, with respect to passive and active inference attackers, and assuming different adversary prior knowledge. We evaluate our novel white-box membership inference attacks against deep learning algorithms to trace their training data records. We show that a straightforward extension of the known black-box attacks to the white-box setting (through analyzing the outputs of activation functions) is ineffective. We therefore design new algorithms tailored to the white-box setting by exploiting the privacy vulnerabilities of the stochastic gradient descent algorithm, which is the algorithm used to train deep neural networks. We investigate the reasons why deep learning models may leak information about their training data. We then show that even well-generalized models are significantly susceptible to white-box membership inference attacks, by analyzing state-of-the-art pre-trained and publicly available models for the CIFAR dataset. We also show how adversarial participants, in the federated learning setting, can successfully run active membership inference attacks against other participants, even when the global model achieves high prediction accuracies.
Toward Scalable Neural Dialogue State Tracking Model
Authors: Elnaz Nouri, Ehsan Hosseini-Asl
Date: 2018-12-03 00:00:00
Tasks: Dialogue State Tracking, model, Multi-domain Dialogue State Tracking

The latency in the current neural based dialogue state tracking models
prohibits them from being used efficiently for deployment in production
systems, albeit their highly accurate performance. This paper proposes a new
scalable and accurate neural dialogue state tracking model, based on the
recently proposed Global-Local Self-Attention encoder (GLAD) model by Zhong et
al. which uses global modules to share parameters between estimators for
different types (called slots) of dialogue states, and uses local modules to
learn slot-specific features. By using only one recurrent networks with global
conditioning, compared to (1 + \# slots) recurrent networks with global and
local conditioning used in the GLAD model, our proposed model reduces the
latency in training and inference times by $35\%$ on average, while preserving
performance of belief state tracking, by $97.38\%$ on turn request and
$88.51\%$ on joint goal and accuracy. Evaluation on Multi-domain dataset
(Multi-WoZ) also demonstrates that our model outperforms GLAD on turn inform
and joint goal accuracy.
Generating Diverse Programs with Instruction Conditioned Reinforced Adversarial Learning
Authors: Aishwarya Agrawal, Mateusz Malinowski, Felix Hill, Ali Eslami, Oriol Vinyals, tejas kulkarni
Date: 2018-12-03 00:00:00
Tasks: Deep Reinforcement Learning, Diversity, Reinforcement Learning

Advances in Deep Reinforcement Learning have led to agents that perform well
across a variety of sensory-motor domains. In this work, we study the setting
in which an agent must learn to generate programs for diverse scenes
conditioned on a given symbolic instruction. Final goals are specified to our
agent via images of the scenes. A symbolic instruction consistent with the goal
images is used as the conditioning input for our policies. Since a single
instruction corresponds to a diverse set of different but still consistent
end-goal images, the agent needs to learn to generate a distribution over
programs given an instruction. We demonstrate that with simple changes to the
reinforced adversarial learning objective, we can learn instruction conditioned
policies to achieve the corresponding diverse set of goals. Most importantly,
our agent's stochastic policy is shown to more accurately capture the diversity
in the goal distribution than a fixed pixel-based reward function baseline. We
demonstrate the efficacy of our approach on two domains: (1) drawing MNIST
digits with a paint software conditioned on instructions and (2) constructing
scenes in a 3D editor that satisfies a certain instruction.
Domain Alignment with Triplets
Authors: Weijian Deng, Liang Zheng, Jianbin Jiao
Date: 2018-12-03 00:00:00
Tasks: Domain Adaptation, Triplet, Unsupervised Domain Adaptation

Deep domain adaptation methods can reduce the distribution discrepancy by
learning domain-invariant embedddings. However, these methods only focus on
aligning the whole data distributions, without considering the class-level
relations among source and target images. Thus, a target embeddings of a bird
might be aligned to source embeddings of an airplane. This semantic
misalignment can directly degrade the classifier performance on the target
dataset. To alleviate this problem, we present a similarity constrained
alignment (SCA) method for unsupervised domain adaptation. When aligning the
distributions in the embedding space, SCA enforces a similarity-preserving
constraint to maintain class-level relations among the source and target
images, i.e., if a source image and a target image are of the same class label,
their corresponding embeddings are supposed to be aligned nearby, and vise
versa. In the absence of target labels, we assign pseudo labels for target
images. Given labeled source images and pseudo-labeled target images, the
similarity-preserving constraint can be implemented by minimizing the triplet
loss. With the joint supervision of domain alignment loss and
similarity-preserving constraint, we train a network to obtain domain-invariant
embeddings with two critical characteristics, intra-class compactness and
inter-class separability. Extensive experiments conducted on the two datasets
well demonstrate the effectiveness of SCA.
Interpretable Deep Learning under Fire
Authors: Xinyang Zhang, Ningfei Wang, Hua Shen, Shouling Ji, Xiapu Luo, Ting Wang
Date: 2018-12-03 00:00:00
Tasks: Decision Making, Deep Learning

Providing explanations for deep neural network (DNN) models is crucial for their use in security-sensitive domains. A plethora of interpretation models have been proposed to help users understand the inner workings of DNNs: how does a DNN arrive at a specific decision for a given input? The improved interpretability is believed to offer a sense of security by involving human in the decision-making process. Yet, due to its data-driven nature, the interpretability itself is potentially susceptible to malicious manipulations, about which little is known thus far. Here we bridge this gap by conducting the first systematic study on the security of interpretable deep learning systems (IDLSes). We show that existing \imlses are highly vulnerable to adversarial manipulations. Specifically, we present ADV^2, a new class of attacks that generate adversarial inputs not only misleading target DNNs but also deceiving their coupled interpretation models. Through empirical evaluation against four major types of IDLSes on benchmark datasets and in security-critical applications (e.g., skin cancer diagnosis), we demonstrate that with ADV^2 the adversary is able to arbitrarily designate an input's prediction and interpretation. Further, with both analytical and empirical evidence, we identify the prediction-interpretation gap as one root cause of this vulnerability -- a DNN and its interpretation model are often misaligned, resulting in the possibility of exploiting both models simultaneously. Finally, we explore potential countermeasures against ADV^2, including leveraging its low transferability and incorporating it in an adversarial training framework. Our findings shed light on designing and operating IDLSes in a more secure and informative fashion, leading to several promising research directions.
Iterative Potts minimization for the recovery of signals with discontinuities from indirect measurements -- the multivariate case
Authors: Lukas Kiefer, Martin Storath, Andreas Weinmann
Date: 2018-12-03 00:00:00
Tasks: Electrical Engineering

Signals and images with discontinuities appear in many problems in such diverse areas as biology, medicine, mechanics, and electrical engineering. The concrete data are often discrete, indirect and noisy measurements of some quantities describing the signal under consideration. A frequent task is to find the segments of the signal or image which corresponds to finding the discontinuities or jumps in the data. Methods based on minimizing the piecewise constant Mumford-Shah functional -- whose discretized version is known as Potts functional -- are advantageous in this scenario, in particular, in connection with segmentation. However, due to their non-convexity, minimization of such functionals is challenging. In this paper we propose a new iterative minimization strategy for the multivariate Potts functional dealing with indirect, noisy measurements. We provide a convergence analysis and underpin our findings with numerical experiments.
Thompson Sampling for Noncompliant Bandits
Authors: Andrew Stirn, Tony Jebara
Date: 2018-12-03 00:00:00
Tasks: Thompson Sampling

Thompson sampling, a Bayesian method for balancing exploration and
exploitation in bandit problems, has theoretical guarantees and exhibits strong
empirical performance in many domains. Traditional Thompson sampling, however,
assumes perfect compliance, where an agent's chosen action is treated as the
implemented action. This article introduces a stochastic noncompliance model
that relaxes this assumption. We prove that any noncompliance in a 2-armed
Bernoulli bandit increases existing regret bounds. With our noncompliance
model, we derive Thompson sampling variants that explicitly handle both
observed and latent noncompliance. With extensive empirical analysis, we
demonstrate that our algorithms either match or outperform traditional Thompson
sampling in both compliant and noncompliant environments.
Towards Solving Text-based Games by Producing Adaptive Action Spaces
Authors: Ruo Yu Tao, Marc-Alexandre Côté, Xingdi Yuan, Layla El Asri
Date: 2018-12-03 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), text-based games, valid

To solve a text-based game, an agent needs to formulate valid text commands
for a given context and find the ones that lead to success. Recent attempts at
solving text-based games with deep reinforcement learning have focused on the
latter, i.e., learning to act optimally when valid actions are known in
advance. In this work, we propose to tackle the first task and train a model
that generates the set of all valid commands for a given context. We try three
generative models on a dataset generated with Textworld. The best model can
generate valid commands which were unseen at training and achieve high $F_1$
score on the test set.
Early Prediction of Course Grades: Models and Feature Selection
Authors: Hengxuan Li, Collin F. Lynch, Tiffany Barnes
Date: 2018-12-03 00:00:00
Tasks: feature selection, regression

In this paper, we compare predictive models for students' final performance
in a blended course using a set of generic features collected from the first
six weeks of class. These features were extracted from students' online
homework submission logs as well as other online actions. We compare the
effectiveness of 5 different ML algorithms (SVMs, Support Vector Regression,
Decision Tree, Naive Bayes and K-Nearest Neighbor). We found that SVMs
outperform other models and improve when compared to the baseline. This study
demonstrates feasible implementations for predictive models that rely on common
data from blended courses that can be used to monitor students' progress and to
tailor instruction.
Novel Quality Metric for Duration Variability Compensation in Speaker Verification using i-Vectors
Authors: Arnab Poddar, Md Sahidullah, Goutam Saha
Date: 2018-12-03 00:00:00
Tasks: Speaker Verification

Automatic speaker verification (ASV) is the process to recognize persons
using voice as biometric. The ASV systems show considerable recognition
performance with sufficient amount of speech from matched condition. One of the
crucial challenges of ASV technology is to improve recognition performance with
speech segments of short duration. In short duration condition, the model
parameters are not properly estimated due to inadequate speech information, and
this results poor recognition accuracy even with the state-of-the-art i-vector
based ASV system. We hypothesize that considering the estimation quality during
recognition process would help to improve the ASV performance. This can be
incorporated as a quality measure during fusion of ASV systems. This paper
investigates a new quality measure for i-vector representation of speech
utterances computed directly from Baum-Welch statistics. The proposed metric is
subsequently used as quality measure during fusion of ASV systems. In
experiments with the NIST SRE 2008 corpus, We have shown that inclusion of
proposed quality metric exhibits considerable improvement in speaker
verification performance. The results also indicate the potentiality of the
proposed method in real-world scenario with short test utterances.
Comparing Neural- and N-Gram-Based Language Models for Word Segmentation
Authors: Yerai Doval, Carlos Gómez-Rodríguez
Date: 2018-12-03 00:00:00
Tasks: Language Modeling, Language Modelling

Word segmentation is the task of inserting or deleting word boundary
characters in order to separate character sequences that correspond to words in
some language. In this article we propose an approach based on a beam search
algorithm and a language model working at the byte/character level, the latter
component implemented either as an n-gram model or a recurrent neural network.
The resulting system analyzes the text input with no word boundaries one token
at a time, which can be a character or a byte, and uses the information
gathered by the language model to determine if a boundary must be placed in the
current position or not. Our aim is to use this system in a preprocessing step
for a microtext normalization system. This means that it needs to effectively
cope with the data sparsity present on this kind of texts. We also strove to
surpass the performance of two readily available word segmentation systems: The
well-known and accessible Word Breaker by Microsoft, and the Python module
WordSegment by Grant Jenks. The results show that we have met our objectives,
and we hope to continue to improve both the precision and the efficiency of our
system in the future.
A Wasserstein GAN model with the total variational regularization
Authors: Lijun Zhang, Yu-Jin Zhang, Yongbin Gao
Date: 2018-12-03 00:00:00
Tasks: Diversity

It is well known that the generative adversarial nets (GANs) are remarkably
difficult to train. The recently proposed Wasserstein GAN (WGAN) creates
principled research directions towards addressing these issues. But we found in
practice that gradient penalty WGANs (GP-WGANs) still suffer from training
instability. In this paper, we combine a Total Variational (TV) regularizing
term into the WGAN formulation instead of weight clipping or gradient penalty,
which implies that the Lipschitz constraint is enforced on the critic network.
Our proposed method is more stable at training than GP-WGANs and works well
across varied GAN architectures. We also present a method to control the
trade-off between image diversity and visual quality. It does not bring any
computation burden.
Towards Spectral Estimation from a Single RGB Image in the Wild
Authors: Berk Kaya, Yigit Baran Can, Radu Timofte
Date: 2018-12-03 00:00:00
Tasks: Sensitivity, Spectral Estimation From A Single Rgb Image, Spectral Reconstruction

In contrast to the current literature, we address the problem of estimating
the spectrum from a single common trichromatic RGB image obtained under
unconstrained settings (e.g. unknown camera parameters, unknown scene radiance,
unknown scene contents). For this we use a reference spectrum as provided by a
hyperspectral image camera, and propose efficient deep learning solutions for
sensitivity function estimation and spectral reconstruction from a single RGB
image. We further expand the concept of spectral reconstruction such that to
work for RGB images taken in the wild and propose a solution based on a
convolutional network conditioned on the estimated sensitivity function.
Besides the proposed solutions, we study also generic and sensitivity
specialized models and discuss their limitations. We achieve state-of-the-art
competitive results on the standard example-based spectral reconstruction
benchmarks: ICVL, CAVE, NUS and NTIRE. Moreover, our experiments show that, for
the first time, accurate spectral estimation from a single RGB image in the
wild is within our reach.
Deep Inverse Optimization
Authors: Yingcong Tan, Andrew Delong, Daria Terekhov
Date: 2018-12-03 00:00:00
Tasks: Deep Learning

Given a set of observations generated by an optimization process, the goal of
inverse optimization is to determine likely parameters of that process. We cast
inverse optimization as a form of deep learning. Our method, called deep
inverse optimization, is to unroll an iterative optimization process and then
use backpropagation to learn parameters that generate the observations. We
demonstrate that by backpropagating through the interior point algorithm we can
learn the coefficients determining the cost vector and the constraints,
independently or jointly, for both non-parametric and parametric linear
programs, starting from one or multiple observations. With this approach,
inverse optimization can leverage concepts and algorithms from deep learning.