pwc-archive/papers-with-abstracts huggingface dataset:
DRIT++: Diverse Image-to-Image Translation via Disentangled Representations
Authors: Hsin-Ying Lee, Hung-Yu Tseng, Qi Mao, Jia-Bin Huang, Yu-Ding Lu, Maneesh Singh, Ming-Hsuan Yang
Date: 2019-05-02 00:00:00
Tasks: Attribute, Diversity, Image-to-Image Translation, Perceptual Distance, Translation

Image-to-image translation aims to learn the mapping between two visual domains. There are two main challenges for this task: 1) lack of aligned training pairs and 2) multiple possible outputs from a single input image. In this work, we present an approach based on disentangled representation for generating diverse outputs without paired training images. To synthesize diverse outputs, we propose to embed images onto two spaces: a domain-invariant content space capturing shared information across domains and a domain-specific attribute space. Our model takes the encoded content features extracted from a given input and attribute vectors sampled from the attribute space to synthesize diverse outputs at test time. To handle unpaired training data, we introduce a cross-cycle consistency loss based on disentangled representations. Qualitative results show that our model can generate diverse and realistic images on a wide range of tasks without paired training data. For quantitative evaluations, we measure realism with user study and Fr\'{e}chet inception distance, and measure diversity with the perceptual distance metric, Jensen-Shannon divergence, and number of statistically-different bins.
Adversarial Training with Voronoi Constraints
Authors: Marc Khoury, Dylan Hadfield-Menell
Date: 2019-05-02 00:00:00

Adversarial examples are a pervasive phenomenon of machine learning models where seemingly imperceptible perturbations to the input lead to misclassifications for otherwise statistically accurate models. We propose a geometric framework, drawing on tools from the manifold reconstruction literature, to analyze the high-dimensional geometry of adversarial examples. In particular, we highlight the importance of codimension: for low-dimensional data manifolds embedded in high-dimensional space there are many directions off the manifold in which an adversary could construct adversarial examples. Adversarial examples are a natural consequence of learning a decision boundary that classifies the low-dimensional data manifold well, but classifies points near the manifold incorrectly. Using our geometric framework we prove that adversarial training is sample inefficient, and show sufficient sampling conditions under which nearest neighbor classifiers and ball-based adversarial training are robust. Finally we introduce adversarial training with Voronoi constraints, which replaces the norm ball constraint with the Voronoi cell for each point in the training set. We show that adversarial training with Voronoi constraints produces robust models which significantly improve over the state-of-the-art on MNIST and are competitive on CIFAR-10.
SinGAN: Learning a Generative Model from a Single Natural Image
Authors: Tamar Rott Shaham, Tali Dekel, Tomer Michaeli
Date: 2019-05-02 00:00:00
Proceeding: ICCV 2019 10
Tasks: Image Generation, Image Manipulation, Image Super-Resolution, Super-Resolution

We introduce SinGAN, an unconditional generative model that can be learned from a single natural image. Our model is trained to capture the internal distribution of patches within the image, and is then able to generate high quality, diverse samples that carry the same visual content as the image. SinGAN contains a pyramid of fully convolutional GANs, each responsible for learning the patch distribution at a different scale of the image. This allows generating new samples of arbitrary size and aspect ratio, that have significant variability, yet maintain both the global structure and the fine textures of the training image. In contrast to previous single image GAN schemes, our approach is not limited to texture images, and is not conditional (i.e. it generates samples from noise). User studies confirm that the generated samples are commonly confused to be real images. We illustrate the utility of SinGAN in a wide range of image manipulation tasks.
Physicist's Journeys Through the AI World - A Topical Review. There is no royal road to unsupervised learning
Authors: Imad Alhousseini, Wissam Chemissany, Fatima Kleit, Aly Nasrallah
Date: 2019-05-02 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning

Artificial Intelligence (AI), defined in its most simple form, is a
technological tool that makes machines intelligent. Since learning is at the
core of intelligence, machine learning poses itself as a core sub-field of AI.
Then there comes a subclass of machine learning, known as deep learning, to
address the limitations of their predecessors. AI has generally acquired its
prominence over the past few years due to its considerable progress in various
fields. AI has vastly invaded the realm of research. This has led physicists to
attentively direct their research towards implementing AI tools. Their central
aim has been to gain better understanding and enrich their intuition. This
review article is meant to supplement the previously presented efforts to
bridge the gap between AI and physics, and take a serious step forward to
filter out the "Babelian" clashes brought about from such gabs. This
necessitates first to have fundamental knowledge about common AI tools. To this
end, the review's primary focus shall be on deep learning models called
artificial neural networks. They are deep learning models which train
themselves through different learning processes. It discusses also the concept
of Markov decision processes. Finally, shortcut to the main goal, the review
thoroughly examines how these neural networks are capable to construct a
physical theory describing some observations without applying any previous
physical knowledge.
Three-Stage Subspace Clustering Framework with Graph-Based Transformation and Optimization
Authors: Shuai Yang, Wenqi Zhu, Yuesheng Zhu
Date: 2019-05-02 00:00:00
Tasks: Clustering

Subspace clustering (SC) refers to the problem of clustering high-dimensional
data into a union of low-dimensional subspaces. Based on spectral clustering,
state-of-the-art approaches solve SC problem within a two-stage framework. In
the first stage, data representation techniques are applied to draw an affinity
matrix from the original data. In the second stage, spectral clustering is
directly applied to the affinity matrix so that data can be grouped into
different subspaces. However, the affinity matrix obtained in the first stage
usually fails to reveal the authentic relationship between data points, which
leads to inaccurate clustering results. In this paper, we propose a universal
Three-Stage Subspace Clustering framework (3S-SC). Graph-Based Transformation
and Optimization (GBTO) is added between data representation and spectral
clustering. The affinity matrix is obtained in the first stage, then it goes
through the second stage, where the proposed GBTO is applied to generate a
reconstructed affinity matrix with more authentic similarity between data
points. Spectral clustering is applied after GBTO, which is the third stage. We
verify our 3S-SC framework with GBTO through theoretical analysis. Experiments
on both synthetic data and the real-world data sets of handwritten digits and
human faces demonstrate the universality of the proposed 3S-SC framework in
improving the connectivity and accuracy of SC methods based on $\ell_0$,
$\ell_1$, $\ell_2$ or nuclear norm regularization.
A Feature Learning Siamese Model for Intelligent Control of the Dynamic Range Compressor
Authors: Di Sheng, György Fazekas
Date: 2019-05-01 00:00:00

In this paper, a siamese DNN model is proposed to learn the characteristics
of the audio dynamic range compressor (DRC). This facilitates an intelligent
control system that uses audio examples to configure the DRC, a widely used
non-linear audio signal conditioning technique in the areas of music
production, speech communication and broadcasting. Several alternative siamese
DNN architectures are proposed to learn feature embeddings that can
characterise subtle effects due to dynamic range compression. These models are
compared with each other as well as handcrafted features proposed in previous
work. The evaluation of the relations between the hyperparameters of DNN and
DRC parameters are also provided. The best model is able to produce a universal
feature embedding that is capable of predicting multiple DRC parameters
simultaneously, which is a significant improvement from our previous research.
The feature embedding shows better performance than handcrafted audio features
when predicting DRC parameters for both mono-instrument audio loops and
polyphonic music pieces.
An Elo-based rating system for TopCoder SRM
Authors: Fred Batty, Dmitry Kamenetsky
Date: 2019-05-01 00:00:00

We present an Elo-based rating system for programming contests. We justify a definition of performance using the logarithm of a player's rank. We apply the definition to rating TopCoder SRM. We improve the accuracy, guided by experimental results. We compare results with SRM ratings.
Semi-supervised Sequence-to-sequence ASR using Unpaired Speech and Text
Authors: Murali Karthick Baskar, Shinji Watanabe, Ramon Astudillo, Takaaki Hori, Lukáš Burget, Jan Černocký
Date: 2019-04-30 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Semi-Supervised Image Classification, speech-recognition, Speech Recognition, text-to-speech, Text to Speech

Sequence-to-sequence automatic speech recognition (ASR) models require large quantities of data to attain high performance. For this reason, there has been a recent surge in interest for unsupervised and semi-supervised training in such models. This work builds upon recent results showing notable improvements in semi-supervised training using cycle-consistency and related techniques. Such techniques derive training procedures and losses able to leverage unpaired speech and/or text data by combining ASR with Text-to-Speech (TTS) models. In particular, this work proposes a new semi-supervised loss combining an end-to-end differentiable ASR$\rightarrow$TTS loss with TTS$\rightarrow$ASR loss. The method is able to leverage both unpaired speech and text data to outperform recently proposed related techniques in terms of \%WER. We provide extensive results analyzing the impact of data quantity and speech and text modalities and show consistent gains across WSJ and Librispeech corpora. Our code is provided in ESPnet to reproduce the experiments.
Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey
Authors: Hamed Jelodar, Yongli Wang, Chi Yuan, Xia Feng, Xiahui Jiang, Yanchao Li, Liang Zhao
Date: 2017-11-12 00:00:00
Tasks: Articles

Topic modeling is one of the most powerful techniques in text mining for data
mining, latent data discovery, and finding relationships among data, text
documents. Researchers have published many articles in the field of topic
modeling and applied in various fields such as software engineering, political
science, medical and linguistic science, etc. There are various methods for
topic modeling, which Latent Dirichlet allocation (LDA) is one of the most
popular methods in this field. Researchers have proposed various models based
on the LDA in topic modeling. According to previous work, this paper can be
very useful and valuable for introducing LDA approaches in topic modeling. In
this paper, we investigated scholarly articles highly (between 2003 to 2016)
related to Topic Modeling based on LDA to discover the research development,
current trends and intellectual structure of topic modeling. Also, we summarize
challenges and introduce famous tools and datasets in topic modeling based on
LDA.
A nucleobase-centered coarse-grained representation for structure prediction of RNA motifs
Authors: Simón Poblete, Sandro Bottaro, Giovanni Bussi
Date: 2017-11-02 00:00:00

We introduce the SPlit-and-conQueR (SPQR) model, a coarse-grained
representation of RNA designed for structure prediction and refinement. In our
approach, the representation of a nucleotide consists of a point particle for
the phosphate group and an anisotropic particle for the nucleoside. The
interactions are, in principle, knowledge-based potentials inspired by the
ESCORE function, a base-centered scoring function. However, a special treatment
is given to base-pairing interactions and certain geometrical conformations
which are lost in a raw knowledge-base model. This results in a representation
able to describe planar canonical and non-canonical base pairs and
base-phosphate interactions and to distinguish sugar puckers and glycosidic
torsion conformations. The model is applied to the folding of several
structures, including duplexes with internal loops of non-canonical base pairs,
tetraloops, junctions and a pseudoknot. For the majority of these systems,
experimental structures are correctly predicted at the level of individual
contacts. We also propose a method for efficiently reintroducing atomistic
detail from the coarse-grained representation.
Computing High Accuracy Power Spectra with Pico
Authors: William A. Fendt, Benjamin D. Wandelt
Date: 2007-12-02 00:00:00
Tasks: Distributed Computing, PICO, Vocal Bursts Intensity Prediction

This paper presents the second release of Pico (Parameters for the Impatient
COsmologist). Pico is a general purpose machine learning code which we have
applied to computing the CMB power spectra and the WMAP likelihood. For this
release, we have made improvements to the algorithm as well as the data sets
used to train Pico, leading to a significant improvement in accuracy. For the 9
parameter nonflat case presented here Pico can on average compute the TT, TE
and EE spectra to better than 1% of cosmic standard deviation for nearly all
$\ell$ values over a large region of parameter space. Performing a cosmological
parameter analysis of current CMB and large scale structure data, we show that
these power spectra give very accurate 1 and 2 dimensional parameter
posteriors. We have extended Pico to allow computation of the tensor power
spectrum and the matter transfer function. Pico runs about 1500 times faster
than CAMB at the default accuracy and about 250,000 times faster at high
accuracy. Training Pico can be done using massively parallel computing
resources, including distributed computing projects such as Cosmology@Home. On
the homepage for Pico, located at http://cosmos.astro.uiuc.edu/pico, we provide
new sets of regression coefficients and make the training code available for
public use.
Representation Learning for Attributed Multiplex Heterogeneous Network
Authors: Yukuo Cen, Xu Zou, Jianwei Zhang, Hongxia Yang, Jingren Zhou, Jie Tang
Date: 2019-05-05 00:00:00
Tasks: Graph Embedding, Inductive Learning, Link Prediction, Network Embedding, Product Recommendation, Representation Learning

Network embedding (or graph embedding) has been widely used in many real-world applications. However, existing methods mainly focus on networks with single-typed nodes/edges and cannot scale well to handle large networks. Many real-world networks consist of billions of nodes and edges of multiple types, and each node is associated with different attributes. In this paper, we formalize the problem of embedding learning for the Attributed Multiplex Heterogeneous Network and propose a unified framework to address this problem. The framework supports both transductive and inductive learning. We also give the theoretical analysis of the proposed framework, showing its connection with previous works and proving its better expressiveness. We conduct systematical evaluations for the proposed framework on four different genres of challenging datasets: Amazon, YouTube, Twitter, and Alibaba. Experimental results demonstrate that with the learned embeddings from the proposed framework, we can achieve statistically significant improvements (e.g., 5.99-28.23% lift by F1 scores; p<<0.01, t-test) over previous state-of-the-art methods for link prediction. The framework has also been successfully deployed on the recommendation system of a worldwide leading e-commerce company, Alibaba Group. Results of the offline A/B tests on product recommendation further confirm the effectiveness and efficiency of the framework in practice.
SEMEDA: Enhancing Segmentation Precision with Semantic Edge Aware Loss
Authors: Yifu Chen, Arnaud Dapogny, Matthieu Cord
Date: 2019-05-06 00:00:00
Tasks: Edge Detection, Segmentation, Semantic Segmentation

While nowadays deep neural networks achieve impressive performances on semantic segmentation tasks, they are usually trained by optimizing pixel-wise losses such as cross-entropy. As a result, the predictions outputted by such networks usually struggle to accurately capture the object boundaries and exhibit holes inside the objects. In this paper, we propose a novel approach to improve the structure of the predicted segmentation masks. We introduce a novel semantic edge detection network, which allows to match the predicted and ground truth segmentation masks. This Semantic Edge-Aware strategy (SEMEDA) can be combined with any backbone deep network in an end-to-end training framework. Through thorough experimental validation on Pascal VOC 2012 and Cityscapes datasets, we show that the proposed SEMEDA approach enhances the structure of the predicted segmentation masks by enforcing sharp boundaries and avoiding discontinuities inside objects, improving the segmentation performance. In addition, our semantic edge-aware loss can be integrated into any popular segmentation network without requiring any additional annotation and with negligible computational load, as compared to standard pixel-wise cross-entropy loss.
CARAFE: Content-Aware ReAssembly of FEatures
Authors: Jiaqi Wang, Kai Chen, Rui Xu, Ziwei Liu, Chen Change Loy, Dahua Lin
Date: 2019-05-06 00:00:00
Proceeding: ICCV 2019 10
Tasks: Feature Upsampling, Instance Segmentation, object-detection, Object Detection, Semantic Segmentation

Feature upsampling is a key operation in a number of modern convolutional network architectures, e.g. feature pyramids. Its design is critical for dense prediction tasks such as object detection and semantic/instance segmentation. In this work, we propose Content-Aware ReAssembly of FEatures (CARAFE), a universal, lightweight and highly effective operator to fulfill this goal. CARAFE has several appealing properties: (1) Large field of view. Unlike previous works (e.g. bilinear interpolation) that only exploit sub-pixel neighborhood, CARAFE can aggregate contextual information within a large receptive field. (2) Content-aware handling. Instead of using a fixed kernel for all samples (e.g. deconvolution), CARAFE enables instance-specific content-aware handling, which generates adaptive kernels on-the-fly. (3) Lightweight and fast to compute. CARAFE introduces little computational overhead and can be readily integrated into modern network architectures. We conduct comprehensive evaluations on standard benchmarks in object detection, instance/semantic segmentation and inpainting. CARAFE shows consistent and substantial gains across all the tasks (1.2%, 1.3%, 1.8%, 1.1db respectively) with negligible computational overhead. It has great potential to serve as a strong building block for future research. It has great potential to serve as a strong building block for future research. Code and models are available at https://github.com/open-mmlab/mmdetection.
Label-Noise Robust Multi-Domain Image-to-Image Translation
Authors: Takuhiro Kaneko, Tatsuya Harada
Date: 2019-05-06 00:00:00
Tasks: Image-to-Image Translation, Translation

Multi-domain image-to-image translation is a problem where the goal is to learn mappings among multiple domains. This problem is challenging in terms of scalability because it requires the learning of numerous mappings, the number of which increases proportional to the number of domains. However, generative adversarial networks (GANs) have emerged recently as a powerful framework for this problem. In particular, label-conditional extensions (e.g., StarGAN) have become a promising solution owing to their ability to address this problem using only a single unified model. Nonetheless, a limitation is that they rely on the availability of large-scale clean-labeled data, which are often laborious or impractical to collect in a real-world scenario. To overcome this limitation, we propose a novel model called the label-noise robust image-to-image translation model (RMIT) that can learn a clean label conditional generator even when noisy labeled data are only available. In particular, we propose a novel loss called the virtual cycle consistency loss that is able to regularize cyclic reconstruction independently of noisy labeled data, as well as we introduce advanced techniques to boost the performance in practice. Our experimental results demonstrate that RMIT is useful for obtaining label-noise robustness in various settings including synthetic and real-world noise.
Computation of Circular Area and Spherical Volume Invariants via Boundary Integrals
Authors: Riley O'Neill, Pedro Angulo-Umana, Jeff Calder, Bo Hessburg, Peter J. Olver, Chehrzad Shakiban, Katrina Yezzi-Woodley
Date: 2019-05-06 00:00:00

We show how to compute the circular area invariant of planar curves, and the spherical volume invariant of surfaces, in terms of line and surface integrals, respectively. We use the Divergence Theorem to express the area and volume integrals as line and surface integrals, respectively, against particular kernels; our results also extend to higher dimensional hypersurfaces. The resulting surface integrals are computable analytically on a triangulated mesh. This gives a simple computational algorithm for computing the spherical volume invariant for triangulated surfaces that does not involve discretizing the ambient space. We discuss potential applications to feature detection on broken bone fragments of interest in anthropology.
Adversarial Examples Are Not Bugs, They Are Features
Authors: Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, Aleksander Madry
Date: 2019-05-06 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: BIG-bench Machine Learning

Adversarial examples have attracted significant attention in machine learning, but the reasons for their existence and pervasiveness remain unclear. We demonstrate that adversarial examples can be directly attributed to the presence of non-robust features: features derived from patterns in the data distribution that are highly predictive, yet brittle and incomprehensible to humans. After capturing these features within a theoretical framework, we establish their widespread existence in standard datasets. Finally, we present a simple setting where we can rigorously tie the phenomena we observe in practice to a misalignment between the (human-specified) notion of robustness and the inherent geometry of the data.