pwc-archive/papers-with-abstracts huggingface dataset:
Adversarial Dropout for Recurrent Neural Networks
Authors: Sungrae Park, Kyungwoo Song, Mingi Ji, Wonsung Lee, Il-Chul Moon
Date: 2019-04-22 00:00:00
Tasks: Language Modeling, Language Modelling, Semi-Supervised Text Classification, Text Classification

Successful application processing sequential data, such as text and speech,
requires an improved generalization performance of recurrent neural networks
(RNNs). Dropout techniques for RNNs were introduced to respond to these
demands, but we conjecture that the dropout on RNNs could have been improved by
adopting the adversarial concept. This paper investigates ways to improve the
dropout for RNNs by utilizing intentionally generated dropout masks.
Specifically, the guided dropout used in this research is called as adversarial
dropout, which adversarially disconnects neurons that are dominantly used to
predict correct targets over time. Our analysis showed that our regularizer,
which consists of a gap between the original and the reconfigured RNNs, was the
upper bound of the gap between the training and the inference phases of the
random dropout. We demonstrated that minimizing our regularizer improved the
effectiveness of the dropout for RNNs on sequential MNIST tasks,
semi-supervised text classification tasks, and language modeling tasks.
Algorithm Portfolio for Individual-based Surrogate-Assisted Evolutionary Algorithms
Authors: Hao Tong, Jialin Liu, Xin Yao
Date: 2019-04-22 00:00:00
Tasks: Evolutionary Algorithms, Reinforcement Learning

Surrogate-assisted evolutionary algorithms (SAEAs) are powerful optimisation tools for computationally expensive problems (CEPs). However, a randomly selected algorithm may fail in solving unknown problems due to no free lunch theorems, and it will cause more computational resource if we re-run the algorithm or try other algorithms to get a much solution, which is more serious in CEPs. In this paper, we consider an algorithm portfolio for SAEAs to reduce the risk of choosing an inappropriate algorithm for CEPs. We propose two portfolio frameworks for very expensive problems in which the maximal number of fitness evaluations is only 5 times of the problem's dimension. One framework named Par-IBSAEA runs all algorithm candidates in parallel and a more sophisticated framework named UCB-IBSAEA employs the Upper Confidence Bound (UCB) policy from reinforcement learning to help select the most appropriate algorithm at each iteration. An effective reward definition is proposed for the UCB policy. We consider three state-of-the-art individual-based SAEAs on different problems and compare them to the portfolios built from their instances on several benchmark problems given limited computation budgets. Our experimental studies demonstrate that our proposed portfolio frameworks significantly outperform any single algorithm on the set of benchmark problems.
Machine Learning Based Analysis of Finnish World War II Photographers
Authors: Kateryna Chumachenko, Anssi Männistö, Alexandros Iosifidis, Jenni Raitoharju
Date: 2019-04-22 00:00:00
Tasks: BIG-bench Machine Learning

In this paper, we demonstrate the benefits of using state-of-the-art machine learning methods in the analysis of historical photo archives. Specifically, we analyze prominent Finnish World War II photographers, who have captured high numbers of photographs in the publicly available Finnish Wartime Photograph Archive, which contains 160,000 photographs from Finnish Winter, Continuation, and Lapland Wars captures in 1939-1945. We were able to find some special characteristics for different photographers in terms of their typical photo content and framing (e.g., close-ups vs. overall shots, number of people). Furthermore, we managed to train a neural network that can successfully recognize the photographer from some of the photos, which shows that such photos are indeed characteristic for certain photographers. We further analyzed the similarities and differences between the photographers using the features extracted from the photographer classifier network. We make our annotations and analysis pipeline publicly available, in an effort to introduce this new research problem to the machine learning and computer vision communities and facilitate future research in historical and societal studies over the photo archives.
Revisiting Multi-Step Nonlinearity Compensation with Machine Learning
Authors: Christian Häger, Henry D. Pfister, Rick M. Bütler, Gabriele Liga, Alex Alvarado
Date: 2019-04-22 00:00:00
Tasks: BIG-bench Machine Learning

For the efficient compensation of fiber nonlinearity, one of the guiding
principles appears to be: fewer steps are better and more efficient. We
challenge this assumption and show that carefully designed multi-step
approaches can lead to better performance-complexity trade-offs than their
few-step counterparts.
Towards Efficient and Secure Delivery of Data for Training and Inference with Privacy-Preserving
Authors: Juncheng Shen, Juzheng Liu, Yiran Chen, Hai Li
Date: 2018-09-20 00:00:00
Tasks: Deep Learning, Privacy Preserving

Privacy recently emerges as a severe concern in deep learning, that is, sensitive data must be prohibited from being shared with the third party during deep neural network development. In this paper, we propose Morphed Learning (MoLe), an efficient and secure scheme to deliver deep learning data. MoLe has two main components: data morphing and Augmented Convolutional (Aug-Conv) layer. Data morphing allows data providers to send morphed data without privacy information, while Aug-Conv layer helps deep learning developers to apply their networks on the morphed data without performance penalty. MoLe provides stronger security while introducing lower overhead compared to GAZELLE (USENIX Security 2018), which is another method with no performance penalty on the neural network. When using MoLe for VGG-16 network on CIFAR dataset, the computational overhead is only 9% and the data transmission overhead is 5.12%. As a comparison, GAZELLE has computational overhead of 10,000 times and data transmission overhead of 421,000 times. In this setting, the attack success rate of adversary is 7.9 x 10^{-90} for MoLe and 2.9 x 10^{-30} for GAZELLE, respectively.
blessing in disguise: Designing Robust Turing Test by Employing Algorithm Unrobustness
Authors: Jiaming Zhang, Jitao Sang, Kaiyuan Xu, Shangxi Wu, Yongli Hu, Yanfeng Sun, Jian Yu
Date: 2019-04-22 00:00:00
Tasks: Adversarial Attack

Turing test was originally proposed to examine whether machine's behavior is
indistinguishable from a human. The most popular and practical Turing test is
CAPTCHA, which is to discriminate algorithm from human by offering
recognition-alike questions. The recent development of deep learning has
significantly advanced the capability of algorithm in solving CAPTCHA
questions, forcing CAPTCHA designers to increase question complexity. Instead
of designing questions difficult for both algorithm and human, this study
attempts to employ the limitations of algorithm to design robust CAPTCHA
questions easily solvable to human. Specifically, our data analysis observes
that human and algorithm demonstrates different vulnerability to visual
distortions: adversarial perturbation is significantly annoying to algorithm
yet friendly to human. We are motivated to employ adversarially perturbed
images for robust CAPTCHA design in the context of character-based questions.
Three modules of multi-target attack, ensemble adversarial training, and image
preprocessing differentiable approximation are proposed to address the
characteristics of character-based CAPTCHA cracking. Qualitative and
quantitative experimental results demonstrate the effectiveness of the proposed
solution. We hope this study can lead to the discussions around adversarial
attack/defense in CAPTCHA design and also inspire the future attempts in
employing algorithm limitation for practical usage.
PCAN: 3D Attention Map Learning Using Contextual Information for Point Cloud Based Retrieval
Authors: Wenxiao Zhang, Chunxia Xiao
Date: 2019-04-22 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Place Recognition, Point Cloud Retrieval, Retrieval, Visual Place Recognition

Point cloud based retrieval for place recognition is an emerging problem in
vision field. The main challenge is how to find an efficient way to encode the
local features into a discriminative global descriptor. In this paper, we
propose a Point Contextual Attention Network (PCAN), which can predict the
significance of each local point feature based on point context. Our network
makes it possible to pay more attention to the task-relevent features when
aggregating local features. Experiments on various benchmark datasets show that
the proposed network can provide outperformance than current state-of-the-art
approaches.
A Unified Framework for Structured Graph Learning via Spectral Constraints
Authors: Sandeep Kumar, Jiaxi Ying, José Vinícius de M. Cardoso, Daniel Palomar
Date: 2019-04-22 00:00:00
Tasks: Graph Learning

Graph learning from data represents a canonical problem that has received
substantial attention in the literature. However, insufficient work has been
done in incorporating prior structural knowledge onto the learning of
underlying graphical models from data. Learning a graph with a specific
structure is essential for interpretability and identification of the
relationships among data. Useful structured graphs include the multi-component
graph, bipartite graph, connected graph, sparse graph, and regular graph. In
general, structured graph learning is an NP-hard combinatorial problem,
therefore, designing a general tractable optimization method is extremely
challenging. In this paper, we introduce a unified graph learning framework
lying at the integration of Gaussian graphical models and spectral graph
theory. To impose a particular structure on a graph, we first show how to
formulate the combinatorial constraints as an analytical property of the graph
matrix. Then we develop an optimization framework that leverages graph learning
with specific structures via spectral constraints on graph matrices. The
proposed algorithms are provably convergent, computationally efficient, and
practically amenable for numerous graph-based tasks. Extensive numerical
experiments with both synthetic and real data sets illustrate the effectiveness
of the proposed algorithms. The code for all the simulations is made available
as an open source repository.
Fast User-Guided Video Object Segmentation by Interaction-and-Propagation Networks
Authors: Seoung Wug Oh, Joon-Young Lee, Ning Xu, Seon Joo Kim
Date: 2019-04-22 00:00:00
Proceeding: CVPR 2019 6
Tasks: Interactive Video Object Segmentation, Object, Segmentation, Semantic Segmentation, Video Object Segmentation, Video Semantic Segmentation

We present a deep learning method for the interactive video object
segmentation. Our method is built upon two core operations, interaction and
propagation, and each operation is conducted by Convolutional Neural Networks.
The two networks are connected both internally and externally so that the
networks are trained jointly and interact with each other to solve the complex
video object segmentation problem. We propose a new multi-round training scheme
for the interactive video object segmentation so that the networks can learn
how to understand the user's intention and update incorrect estimations during
the training. At the testing time, our method produces high-quality results and
also runs fast enough to work with users interactively. We evaluated the
proposed method quantitatively on the interactive track benchmark at the DAVIS
Challenge 2018. We outperformed other competing methods by a significant margin
in both the speed and the accuracy. We also demonstrated that our method works
well with real user interactions.
Detecting retail products in situ using CNN without human effort labeling
Authors: Wei Yi, Yaoran Sun, Tao Ding, Sailing He
Date: 2019-04-22 00:00:00
Tasks: object-detection, Object Detection

CNN is a powerful tool for many computer vision tasks, achieving much better
result than traditional methods. Since CNN has a very large capacity, training
such a neural network often requires many data, but it is often expensive to
obtain labeled images in real practice, especially for object detection, where
collecting bounding box of every object in training set requires many human
efforts. This is the case in detection of retail products where there can be
many different categories. In this paper, we focus on applying CNN to detect
324-categories products in situ, while requiring no extra effort of labeling
bounding box for any image. Our approach is based on an algorithm that extracts
bounding box from in-vitro dataset and an algorithm to simulate occlusion. We
have successfully shown the effectiveness and usefulness of our methods to
build up a Faster RCNN detection model. Similar idea is also applicable in
other scenarios.
Training generative networks using random discriminators
Authors: Babak Barazandeh, Meisam Razaviyayn, Maziar Sanjabi
Date: 2019-04-22 00:00:00

In recent years, Generative Adversarial Networks (GANs) have drawn a lot of
attentions for learning the underlying distribution of data in various
applications. Despite their wide applicability, training GANs is notoriously
difficult. This difficulty is due to the min-max nature of the resulting
optimization problem and the lack of proper tools of solving general
(non-convex, non-concave) min-max optimization problems. In this paper, we try
to alleviate this problem by proposing a new generative network that relies on
the use of random discriminators instead of adversarial design. This design
helps us to avoid the min-max formulation and leads to an optimization problem
that is stable and could be solved efficiently. The performance of the proposed
method is evaluated using handwritten digits (MNIST) and Fashion products
(Fashion-MNIST) data sets. While the resulting images are not as sharp as
adversarial training, the use of random discriminator leads to a much faster
algorithm as compared to the adversarial counterpart. This observation, at the
minimum, illustrates the potential of the random discriminator approach for
warm-start in training GANs.
Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model
Authors: Erik Nijkamp, Mitch Hill, Song-Chun Zhu, Ying Nian Wu
Date: 2019-04-22 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: valid

This paper studies a curious phenomenon in learning energy-based model (EBM) using MCMC. In each learning iteration, we generate synthesized examples by running a non-convergent, non-mixing, and non-persistent short-run MCMC toward the current model, always starting from the same initial distribution such as uniform noise distribution, and always running a fixed number of MCMC steps. After generating synthesized examples, we then update the model parameters according to the maximum likelihood learning gradient, as if the synthesized examples are fair samples from the current model. We treat this non-convergent short-run MCMC as a learned generator model or a flow model. We provide arguments for treating the learned non-convergent short-run MCMC as a valid model. We show that the learned short-run MCMC is capable of generating realistic images. More interestingly, unlike traditional EBM or MCMC, the learned short-run MCMC is capable of reconstructing observed images and interpolating between images, like generator or flow models. The code can be found in the Appendix.
hf0: A hybrid pitch extraction method for multimodal voice
Authors: Pradeep Rengaswamy, Gurunath Reddy M, Krothapalli Sreenivasa Rao
Date: 2019-04-22 00:00:00
Tasks: Deep Learning

Pitch or fundamental frequency (f0) extraction is a fundamental problem
studied extensively for its potential applications in speech and clinical
applications. In literature, explicit mode specific (modal speech or singing
voice or emotional/ expressive speech or noisy speech) signal processing and
deep learning f0 extraction methods that exploit the quasi periodic nature of
the signal in time, harmonic property in spectral or combined form to extract
the pitch is developed. Hence, there is no single unified method which can
reliably extract the pitch from various modes of the acoustic signal. In this
work, we propose a hybrid f0 extraction method which seamlessly extracts the
pitch across modes of speech production with very high accuracy required for
many applications. The proposed hybrid model exploits the advantages of deep
learning and signal processing methods to minimize the pitch detection error
and adopts to various modes of acoustic signal. Specifically, we propose an
ordinal regression convolutional neural networks to map the periodicity rich
input representation to obtain the nominal pitch classes which drastically
reduces the number of classes required for pitch detection unlike other deep
learning approaches. Further, the accurate f0 is estimated from the nominal
pitch class labels by filtering and autocorrelation. We show that the proposed
method generalizes to the unseen modes of voice production and various noises
for large scale datasets. Also, the proposed hybrid model significantly reduces
the learning parameters required to train the deep model compared to other
methods. Furthermore,the evaluation measures showed that the proposed method is
significantly better than the state-of-the-art signal processing and deep
learning approaches.
Water-Filling: An Efficient Algorithm for Digitized Document Shadow Removal
Authors: Seungjun Jung, Muhammad Abul Hasan, Changick Kim
Date: 2019-04-22 00:00:00
Tasks: Document Shadow Removal, Shadow Removal

In this paper, we propose a novel algorithm to rectify illumination of the digitized documents by eliminating shading artifacts. Firstly, a topographic surface of an input digitized document is created using luminance value of each pixel. Then the shading artifact on the document is estimated by simulating an immersion process. The simulation of the immersion process is modeled using a novel diffusion equation with an iterative update rule. After estimating the shading artifacts, the digitized document is reconstructed using the Lambertian surface model. In order to evaluate the performance of the proposed algorithm, we conduct rigorous experiments on a set of digitized documents which is generated using smartphones under challenging lighting conditions. According to the experimental results, it is found that the proposed method produces promising illumination correction results and outperforms the results of the state-of-the-art methods.
FoxNet: A Multi-face Alignment Method
Authors: Yuxiang Wu, Zehua Cheng, Bin Huang, Yiming Chen, Xinghui Zhu, Weiyang Wang
Date: 2019-04-22 00:00:00
Tasks: Clustering, Face Alignment, Face Recognition

Multi-face alignment aims to identify geometry structures of multiple faces in an image, and its performance is essential for the many practical tasks, such as face recognition, face tracking, and face animation. In this work, we present a fast bottom-up multi-face alignment approach, which can simultaneously localize multi-person facial landmarks with high precision.In more detail, our bottom-up architecture maps the landmarks to the high-dimensional space with which landmarks of all faces are represented. By clustering the features belonging to the same face, our approach can align the multi-person facial landmarks synchronously.Extensive experiments show that our method can achieve high performance in the multi-face landmark alignment task while our model is extremely fast. Moreover, we propose a new multi-face dataset to compare the speed and precision of bottom-up face alignment method with top-down methods. Our dataset is publicly available at https://github.com/AISAResearch/FoxNet
Non-local Attention Optimized Deep Image Compression
Authors: Haojie Liu, Tong Chen, Peiyao Guo, Qiu Shen, Xun Cao, Yao Wang, Zhan Ma
Date: 2019-04-22 00:00:00
Tasks: Image Compression, MS-SSIM, SSIM

This paper proposes a novel Non-Local Attention Optimized Deep Image
Compression (NLAIC) framework, which is built on top of the popular variational
auto-encoder (VAE) structure. Our NLAIC framework embeds non-local operations
in the encoders and decoders for both image and latent feature probability
information (known as hyperprior) to capture both local and global
correlations, and apply attention mechanism to generate masks that are used to
weigh the features for the image and hyperprior, which implicitly adapt bit
allocation for different features based on their importance. Furthermore, both
hyperpriors and spatial-channel neighbors of the latent features are used to
improve entropy coding. The proposed model outperforms the existing methods on
Kodak dataset, including learned (e.g., Balle2019, Balle2018) and conventional
(e.g., BPG, JPEG2000, JPEG) image compression methods, for both PSNR and
MS-SSIM distortion metrics.
The Curious Case of Neural Text Degeneration
Authors: Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, Yejin Choi
Date: 2019-04-22 00:00:00
Proceeding: ICLR 2020 1
Tasks: Diversity, Language Modeling, Language Modelling

Despite considerable advancements with deep neural language models, the enigma of neural text degeneration persists when these models are tested as text generators. The counter-intuitive empirical observation is that even though the use of likelihood as training objective leads to high quality models for a broad range of language understanding tasks, using likelihood as a decoding objective leads to text that is bland and strangely repetitive. In this paper, we reveal surprising distributional differences between human text and machine text. In addition, we find that decoding strategies alone can dramatically effect the quality of machine text, even when generated from exactly the same neural language model. Our findings motivate Nucleus Sampling, a simple but effective method to draw the best out of neural generation. By sampling text from the dynamic nucleus of the probability distribution, which allows for diversity while effectively truncating the less reliable tail of the distribution, the resulting text better demonstrates the quality of human text, yielding enhanced diversity without sacrificing fluency and coherence.
Local Deep-Feature Alignment for Unsupervised Dimension Reduction
Authors: Jian Zhang, Jun Yu, DaCheng Tao
Date: 2019-04-22 00:00:00
Tasks: Clustering, Data Visualization, Dimensionality Reduction

This paper presents an unsupervised deep-learning framework named Local
Deep-Feature Alignment (LDFA) for dimension reduction. We construct
neighbourhood for each data sample and learn a local Stacked Contractive
Auto-encoder (SCAE) from the neighbourhood to extract the local deep features.
Next, we exploit an affine transformation to align the local deep features of
each neighbourhood with the global features. Moreover, we derive an approach
from LDFA to map explicitly a new data sample into the learned low-dimensional
subspace. The advantage of the LDFA method is that it learns both local and
global characteristics of the data sample set: the local SCAEs capture local
characteristics contained in the data set, while the global alignment
procedures encode the interdependencies between neighbourhoods into the final
low-dimensional feature representations. Experimental results on data
visualization, clustering and classification show that the LDFA method is
competitive with several well-known dimension reduction techniques, and
exploiting locality in deep learning is a research topic worth further
exploring.