pwc-archive/papers-with-abstracts huggingface dataset:
Synthetic data generation for end-to-end thermal infrared tracking
Authors: Lichao Zhang, Abel Gonzalez-Garcia, Joost Van de Weijer, Martin Danelljan, Fahad Shahbaz Khan
Date: 2018-06-04 00:00:00
Tasks: Image-to-Image Translation, Synthetic Data Generation, Translation, Visual Tracking

The usage of both off-the-shelf and end-to-end trained deep networks have
significantly improved performance of visual tracking on RGB videos. However,
the lack of large labeled datasets hampers the usage of convolutional neural
networks for tracking in thermal infrared (TIR) images. Therefore, most state
of the art methods on tracking for TIR data are still based on handcrafted
features. To address this problem, we propose to use image-to-image translation
models. These models allow us to translate the abundantly available labeled RGB
data to synthetic TIR data. We explore both the usage of paired and unpaired
image translation models for this purpose. These methods provide us with a
large labeled dataset of synthetic TIR sequences, on which we can train
end-to-end optimal features for tracking. To the best of our knowledge we are
the first to train end-to-end features for TIR tracking. We perform extensive
experiments on VOT-TIR2017 dataset. We show that a network trained on a large
dataset of synthetic TIR data obtains better performance than one trained on
the available real TIR data. Combining both data sources leads to further
improvement. In addition, when we combine the network with motion features we
outperform the state of the art with a relative gain of over 10%, clearly
showing the efficiency of using synthetic data to train end-to-end TIR
trackers.
Exemplar Guided Unsupervised Image-to-Image Translation with Semantic Consistency
Authors: Liqian Ma, Xu Jia, Stamatios Georgoulis, Tinne Tuytelaars, Luc van Gool
Date: 2018-05-28 00:00:00
Proceeding: ICLR 2019 5
Tasks: Image-to-Image Translation, Translation, Unsupervised Image-To-Image Translation

Image-to-image translation has recently received significant attention due to
advances in deep learning. Most works focus on learning either a one-to-one
mapping in an unsupervised way or a many-to-many mapping in a supervised way.
However, a more practical setting is many-to-many mapping in an unsupervised
way, which is harder due to the lack of supervision and the complex inner- and
cross-domain variations. To alleviate these issues, we propose the Exemplar
Guided & Semantically Consistent Image-to-image Translation (EGSC-IT) network
which conditions the translation process on an exemplar image in the target
domain. We assume that an image comprises of a content component which is
shared across domains, and a style component specific to each domain. Under the
guidance of an exemplar from the target domain we apply Adaptive Instance
Normalization to the shared content component, which allows us to transfer the
style information of the target domain to the source domain. To avoid semantic
inconsistencies during translation that naturally appear due to the large
inner- and cross-domain variations, we introduce the concept of feature masks
that provide coarse semantic guidance without requiring the use of any semantic
labels. Experimental results on various datasets show that EGSC-IT does not
only translate the source image to diverse instances in the target domain, but
also preserves the semantic consistency during the process.
Meta-Learner with Linear Nulling
Authors: Sung Whan Yoon, Jun Seo, Jaekyun Moon
Date: 2018-06-04 00:00:00
Tasks: Classification, Few-Shot Learning, General Classification, image-classification, Image Classification, Meta-Learning

We propose a meta-learning algorithm utilizing a linear transformer that
carries out null-space projection of neural network outputs. The main idea is
to construct an alternative classification space such that the error signals
during few-shot learning are quickly zero-forced on that space so that reliable
classification on low data is possible. The final decision on a query is
obtained utilizing a null-space-projected distance measure between the network
output and reference vectors, both of which have been trained in the initial
learning phase. Among the known methods with a given model size, our
meta-learner achieves the best or near-best image classification accuracies
with Omniglot and miniImageNet datasets.
Adversarial Learning of Structure-Aware Fully Convolutional Networks for Landmark Localization
Authors: Yu Chen, Chunhua Shen, Hao Chen, Xiu-Shen Wei, Lingqiao Liu, Jian Yang
Date: 2017-11-01 00:00:00
Tasks: Pose Estimation

Landmark/pose estimation in single monocular images have received much effort
in computer vision due to its important applications. It remains a challenging
task when input images severe occlusions caused by, e.g., adverse camera views.
Under such circumstances, biologically implausible pose predictions may be
produced. In contrast, human vision is able to predict poses by exploiting
geometric constraints of landmark point inter-connectivity. To address the
problem, by incorporating priors about the structure of pose components, we
propose a novel structure-aware fully convolutional network to implicitly take
such priors into account during training of the deep network. Explicit learning
of such constraints is typically challenging. Instead, inspired by how human
identifies implausible poses, we design discriminators to distinguish the real
poses from the fake ones (such as biologically implausible ones). If the pose
generator G generates results that the discriminator fails to distinguish from
real ones, the network successfully learns the priors. Training of the network
follows the strategy of conditional Generative Adversarial Networks (GANs). The
effectiveness of the proposed network is evaluated on three pose-related tasks:
2D single human pose estimation, 2D facial landmark estimation and 3D single
human pose estimation. The proposed approach significantly outperforms the
state-of-the-art methods and almost always generates plausible pose
predictions, demonstrating the usefulness of implicit learning of structures
using GANs.
Distributed Learning from Interactions in Social Networks
Authors: Francesco Sasso, Angelo Coluccia, Giuseppe Notarstefano
Date: 2018-06-04 00:00:00

We consider a network scenario in which agents can evaluate each other
according to a score graph that models some interactions. The goal is to design
a distributed protocol, run by the agents, that allows them to learn their
unknown state among a finite set of possible values. We propose a Bayesian
framework in which scores and states are associated to probabilistic events
with unknown parameters and hyperparameters, respectively. We show that each
agent can learn its state by means of a local Bayesian classifier and a
(centralized) Maximum-Likelihood (ML) estimator of parameter-hyperparameter
that combines plain ML and Empirical Bayes approaches. By using tools from
graphical models, which allow us to gain insight on conditional dependencies of
scores and states, we provide a relaxed probabilistic model that ultimately
leads to a parameter-hyperparameter estimator amenable to distributed
computation. To highlight the appropriateness of the proposed relaxation, we
demonstrate the distributed estimators on a social interaction set-up for user
profiling.
Conditional Probability Models for Deep Image Compression
Authors: Fabian Mentzer, Eirikur Agustsson, Michael Tschannen, Radu Timofte, Luc van Gool
Date: 2018-01-12 00:00:00
Proceeding: CVPR 2018 6
Tasks: Image Compression, MS-SSIM, Navigate, Quantization, SSIM

Deep Neural Networks trained as image auto-encoders have recently emerged as a promising direction for advancing the state-of-the-art in image compression. The key challenge in learning such networks is twofold: To deal with quantization, and to control the trade-off between reconstruction error (distortion) and entropy (rate) of the latent image representation. In this paper, we focus on the latter challenge and propose a new technique to navigate the rate-distortion trade-off for an image compression auto-encoder. The main idea is to directly model the entropy of the latent representation by using a context model: A 3D-CNN which learns a conditional probability model of the latent distribution of the auto-encoder. During training, the auto-encoder makes use of the context model to estimate the entropy of its representation, and the context model is concurrently updated to learn the dependencies between the symbols in the latent representation. Our experiments show that this approach, when measured in MS-SSIM, yields a state-of-the-art image compression system based on a simple convolutional auto-encoder.
Syllable-Based Sequence-to-Sequence Speech Recognition with the Transformer in Mandarin Chinese
Authors: Shiyu Zhou, Linhao Dong, Shuang Xu, Bo Xu
Date: 2018-04-28 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, Language Modeling, Language Modelling, Machine Translation, NMT, Sequence-To-Sequence Speech Recognition, speech-recognition, Speech Recognition, Translation

Sequence-to-sequence attention-based models have recently shown very
promising results on automatic speech recognition (ASR) tasks, which integrate
an acoustic, pronunciation and language model into a single neural network. In
these models, the Transformer, a new sequence-to-sequence attention-based model
relying entirely on self-attention without using RNNs or convolutions, achieves
a new single-model state-of-the-art BLEU on neural machine translation (NMT)
tasks. Since the outstanding performance of the Transformer, we extend it to
speech and concentrate on it as the basic architecture of sequence-to-sequence
attention-based model on Mandarin Chinese ASR tasks. Furthermore, we
investigate a comparison between syllable based model and context-independent
phoneme (CI-phoneme) based model with the Transformer in Mandarin Chinese.
Additionally, a greedy cascading decoder with the Transformer is proposed for
mapping CI-phoneme sequences and syllable sequences into word sequences.
Experiments on HKUST datasets demonstrate that syllable based model with the
Transformer performs better than CI-phoneme based counterpart, and achieves a
character error rate (CER) of \emph{$28.77\%$}, which is competitive to the
state-of-the-art CER of $28.0\%$ by the joint CTC-attention based
encoder-decoder network.
Automatic Goal Generation for Reinforcement Learning Agents
Authors: Carlos Florensa, David Held, Xinyang Geng, Pieter Abbeel
Date: 2017-05-17 00:00:00
Proceeding: ICML 2018 7
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning is a powerful technique to train an agent to perform a
task. However, an agent that is trained using reinforcement learning is only
capable of achieving the single task that is specified via its reward function.
Such an approach does not scale well to settings in which an agent needs to
perform a diverse set of tasks, such as navigating to varying positions in a
room or moving objects to varying locations. Instead, we propose a method that
allows an agent to automatically discover the range of tasks that it is capable
of performing. We use a generator network to propose tasks for the agent to try
to achieve, specified as goal states. The generator network is optimized using
adversarial training to produce tasks that are always at the appropriate level
of difficulty for the agent. Our method thus automatically produces a
curriculum of tasks for the agent to learn. We show that, by using this
framework, an agent can efficiently and automatically learn to perform a wide
set of tasks without requiring any prior knowledge of its environment. Our
method can also learn to achieve tasks with sparse rewards, which traditionally
pose significant challenges.
Asymptotic optimality of adaptive importance sampling
Authors: Bernard Delyon, François Portier
Date: 2018-06-04 00:00:00
Proceeding: NeurIPS 2018 12

Adaptive importance sampling (AIS) uses past samples to update the
\textit{sampling policy} $q_t$ at each stage $t$. Each stage $t$ is formed with
two steps : (i) to explore the space with $n_t$ points according to $q_t$ and
(ii) to exploit the current amount of information to update the sampling
policy. The very fundamental question raised in this paper concerns the
behavior of empirical sums based on AIS. Without making any assumption on the
allocation policy $n_t$, the theory developed involves no restriction on the
split of computational resources between the explore (i) and the exploit (ii)
step. It is shown that AIS is asymptotically optimal : the asymptotic behavior
of AIS is the same as some "oracle" strategy that knows the targeted sampling
policy from the beginning. From a practical perspective, weighted AIS is
introduced, a new method that allows to forget poor samples from early stages.
DNN-HMM based Speaker Adaptive Emotion Recognition using Proposed Epoch and MFCC Features
Authors: Md. Shah Fahad, Jainath Yadav, Gyadhar Pradhan, Akshay Deepak
Date: 2018-06-04 00:00:00
Tasks: Emotion Recognition

Speech is produced when time varying vocal tract system is excited with time
varying excitation source. Therefore, the information present in a speech such
as message, emotion, language, speaker is due to the combined effect of both
excitation source and vocal tract system. However, there is very less
utilization of excitation source features to recognize emotion. In our earlier
work, we have proposed a novel method to extract glottal closure instants
(GCIs) known as epochs. In this paper, we have explored epoch features namely
instantaneous pitch, phase and strength of epochs for discriminating emotions.
We have combined the excitation source features and the well known
Male-frequency cepstral coefficient (MFCC) features to develop an emotion
recognition system with improved performance. DNN-HMM speaker adaptive models
have been developed using MFCC, epoch and combined features. IEMOCAP emotional
database has been used to evaluate the models. The average accuracy for emotion
recognition system when using MFCC and epoch features separately is 59.25% and
54.52% respectively. The recognition performance improves to 64.2% when MFCC
and epoch features are combined.
Automatic Clustering of a Network Protocol with Weakly-Supervised Clustering
Authors: Tobias Schrank, Franz Pernkopf
Date: 2018-06-04 00:00:00
Tasks: Clustering

Abstraction is a fundamental part when learning behavioral models of systems.
Usually the process of abstraction is manually defined by domain experts. This
paper presents a method to perform automatic abstraction for network protocols.
In particular a weakly supervised clustering algorithm is used to build an
abstraction with a small vocabulary size for the widely used TLS protocol. To
show the effectiveness of the proposed method we compare the resultant abstract
messages to a manually constructed (reference) abstraction. With a small amount
of side-information in the form of a few labeled examples this method finds an
abstraction that matches the reference abstraction perfectly.
Similarity encoding for learning with dirty categorical variables
Authors: Patricio Cerda, Gaël Varoquaux, Balázs Kégl
Date: 2018-06-04 00:00:00
Tasks: Dimensionality Reduction

For statistical learning, categorical variables in a table are usually
considered as discrete entities and encoded separately to feature vectors,
e.g., with one-hot encoding. "Dirty" non-curated data gives rise to categorical
variables with a very high cardinality but redundancy: several categories
reflect the same entity. In databases, this issue is typically solved with a
deduplication step. We show that a simple approach that exposes the redundancy
to the learning algorithm brings significant gains. We study a generalization
of one-hot encoding, similarity encoding, that builds feature vectors from
similarities across categories. We perform a thorough empirical validation on
non-curated tables, a problem seldom studied in machine learning. Results on
seven real-world datasets show that similarity encoding brings significant
gains in prediction in comparison with known encoding methods for categories or
strings, notably one-hot encoding and bag of character n-grams. We draw
practical recommendations for encoding dirty categories: 3-gram similarity
appears to be a good choice to capture morphological resemblance. For very
high-cardinality, dimensionality reduction significantly reduces the
computational cost with little loss in performance: random projections or
choosing a subset of prototype categories still outperforms classic encoding
approaches.
Sequential Test for the Lowest Mean: From Thompson to Murphy Sampling
Authors: Emilie Kaufmann, Wouter Koolen, Aurelien Garivier
Date: 2018-06-04 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Reinforcement Learning, Reinforcement Learning (RL), Thompson Sampling

Learning the minimum/maximum mean among a finite set of distributions is a
fundamental sub-task in planning, game tree search and reinforcement learning.
We formalize this learning task as the problem of sequentially testing how the
minimum mean among a finite set of distributions compares to a given threshold.
We develop refined non-asymptotic lower bounds, which show that optimality
mandates very different sampling behavior for a low vs high true minimum. We
show that Thompson Sampling and the intuitive Lower Confidence Bounds policy
each nail only one of these cases. We develop a novel approach that we call
Murphy Sampling. Even though it entertains exclusively low true minima, we
prove that MS is optimal for both possibilities. We then design advanced
self-normalized deviation inequalities, fueling more aggressive stopping rules.
We complement our theoretical guarantees by experiments showing that MS works
best in practice.
Training deep learning based image denoisers from undersampled measurements without ground truth and without image prior
Authors: Magauiya Zhussip, Shakarim Soltanayev, Se Young Chun
Date: 2018-06-04 00:00:00
Proceeding: CVPR 2019 6
Tasks: Compressive Sensing, Deep Learning

Compressive sensing is a method to recover the original image from
undersampled measurements. In order to overcome the ill-posedness of this
inverse problem, image priors are used such as sparsity in the wavelet domain,
minimum total-variation, or self-similarity. Recently, deep learning based
compressive image recovery methods have been proposed and have yielded
state-of-the-art performances. They used deep learning based data-driven
approaches instead of hand-crafted image priors to solve the ill-posed inverse
problem with undersampled data. Ironically, training deep neural networks for
them requires "clean" ground truth images, but obtaining the best quality
images from undersampled data requires well-trained deep neural networks. To
resolve this dilemma, we propose novel methods based on two well-grounded
theories: denoiser-approximate message passing and Stein's unbiased risk
estimator. Our proposed methods were able to train deep learning based image
denoisers from undersampled measurements without ground truth images and
without image priors, and to recover images with state-of-the-art qualities
from undersampled data. We evaluated our methods for various compressive
sensing recovery problems with Gaussian random, coded diffraction pattern, and
compressive sensing MRI measurement matrices. Our methods yielded
state-of-the-art performances for all cases without ground truth images and
without image priors. They also yielded comparable performances to the methods
with ground truth data.
The Capacity Constrained Facility Location problem
Authors: Haris Aziz, Hau Chan, Barton E. Lee, David C. Parkes
Date: 2018-06-04 00:00:00

We initiate the study of the capacity constrained facility location problem
from a mechanism design perspective. The capacity constrained setting leads to
a new strategic environment where a facility serves a subset of the population,
which is endogenously determined by the ex-post Nash equilibrium of an induced
subgame and is not directly controlled by the mechanism designer. Our focus is
on mechanisms that are ex-post dominant-strategy incentive compatible (DIC) at
the reporting stage. We provide a complete characterization of DIC mechanisms
via the family of Generalized Median Mechanisms (GMMs). In general, the social
welfare optimal mechanism is not DIC. Adopting the worst-case approximation
measure, we attain tight lower bounds on the approximation ratio of any DIC
mechanism. The well-known median mechanism is shown to be optimal among the
family of DIC mechanisms for certain capacity ranges. Surprisingly, the
framework we introduce provides a new characterization for the family of GMMs,
and is responsive to gaps in the current social choice literature highlighted
by Border and Jordan (1983) and Barbar{\`a}, Mass{\'o} and Serizawa (1998).
Private PAC learning implies finite Littlestone dimension
Authors: Noga Alon, Roi Livni, Maryanthe Malliaris, Shay Moran
Date: 2018-06-04 00:00:00
Tasks: Open-Ended Question Answering, PAC learning

We show that every approximately differentially private learning algorithm
(possibly improper) for a class $H$ with Littlestone dimension~$d$ requires
$\Omega\bigl(\log^*(d)\bigr)$ examples. As a corollary it follows that the
class of thresholds over $\mathbb{N}$ can not be learned in a private manner;
this resolves open question due to [Bun et al., 2015, Feldman and Xiao, 2015].
We leave as an open question whether every class with a finite Littlestone
dimension can be learned by an approximately differentially private algorithm.
Robustness May Be at Odds with Accuracy
Authors: Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, Aleksander Madry
Date: 2018-05-30 00:00:00
Proceeding: ICLR 2019 5
Tasks: Adversarial Robustness

We show that there may exist an inherent tension between the goal of adversarial robustness and that of standard generalization. Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy. We demonstrate that this trade-off between the standard accuracy of a model and its robustness to adversarial perturbations provably exists in a fairly simple and natural setting. These findings also corroborate a similar phenomenon observed empirically in more complex settings. Further, we argue that this phenomenon is a consequence of robust classifiers learning fundamentally different feature representations than standard classifiers. These differences, in particular, seem to result in unexpected benefits: the representations learned by robust models tend to align better with salient data characteristics and human perception.
Program Synthesis from Visual Specification
Authors: Evan Hernandez, Ara Vartanian, Xiaojin Zhu
Date: 2018-06-04 00:00:00
Tasks: Program Synthesis

Program synthesis is the process of automatically translating a specification
into computer code. Traditional synthesis settings require a formal, precise
specification. Motivated by computer education applications where a student
learns to code simple turtle-style drawing programs, we study a novel synthesis
setting where only a noisy user-intention drawing is specified. This allows
students to sketch their intended output, optionally together with their own
incomplete program, to automatically produce a completed program. We formulate
this synthesis problem as search in the space of programs, with the score of a
state being the Hausdorff distance between the program output and the user
drawing. We compare several search algorithms on a corpus consisting of real
user drawings and the corresponding programs, and demonstrate that our
algorithms can synthesize programs optimally satisfying the specification.