pwc-archive/papers-with-abstracts huggingface dataset:
Handwriting Recognition in Low-resource Scripts using Adversarial Learning
Authors: Ayan Kumar Bhunia, Abhirup Das, Ankan Kumar Bhunia, Perla Sai Raj Kishore, Partha Pratim Roy
Date: 2018-11-04 00:00:00
Proceeding: CVPR 2019 6
Tasks: Handwriting Recognition, Retrieval

Handwritten Word Recognition and Spotting is a challenging field dealing with
handwritten text possessing irregular and complex shapes. The design of deep
neural network models makes it necessary to extend training datasets in order
to introduce variations and increase the number of samples; word-retrieval is
therefore very difficult in low-resource scripts. Much of the existing
literature comprises preprocessing strategies which are seldom sufficient to
cover all possible variations. We propose the Adversarial Feature Deformation
Module (AFDM) that learns ways to elastically warp extracted features in a
scalable manner. The AFDM is inserted between intermediate layers and trained
alternatively with the original framework, boosting its capability to better
learn highly informative features rather than trivial ones. We test our
meta-framework, which is built on top of popular word-spotting and
word-recognition frameworks and enhanced by the AFDM, not only on extensive
Latin word datasets but also sparser Indic scripts. We record results for
varying training data sizes, and observe that our enhanced network generalizes
much better in the low-data regime; the overall word-error rates and mAP scores
are observed to improve as well.
A Deep One-Shot Network for Query-based Logo Retrieval
Authors: Ayan Kumar Bhunia, Ankan Kumar Bhunia, Shuvozit Ghose, Abhirup Das, Partha Pratim Roy, Umapada Pal
Date: 2018-11-04 00:00:00
Tasks: Marketing, object-detection, Object Detection, One-Shot Learning, Retrieval, Segmentation

Logo detection in real-world scene images is an important problem with applications in advertisement and marketing. Existing general-purpose object detection methods require large training data with annotations for every logo class. These methods do not satisfy the incremental demand of logo classes necessary for practical deployment since it is practically impossible to have such annotated data for new unseen logo. In this work, we develop an easy-to-implement query-based logo detection and localization system by employing a one-shot learning technique. Given an image of a query logo, our model searches for it within a given target image and predicts the possible location of the logo by estimating a binary segmentation mask. The proposed model consists of a conditional branch and a segmentation branch. The former gives a conditional latent representation of the given query logo which is combined with feature maps of the segmentation branch at multiple scales in order to find the matching position of the query logo in a target image, should it be present. Feature matching between the latent query representation and multi-scale feature maps of segmentation branch using simple concatenation operation followed by 1x1 convolution layer makes our model scale-invariant. Despite its simplicity, our query-based logo retrieval framework achieved superior performance in FlickrLogos-32 and TopLogos-10 dataset over different existing baselines.
A method to construct exponential families by representation theory
Authors: Koichi Tojo, Taro Yoshino
Date: 2018-11-04 00:00:00

In this paper, we give a method to construct "good" exponential families systematically by representation theory. More precisely, we consider a homogeneous space $G/H$ as a sample space and construct an exponential family invariant under the transformation group $G$ by using a representation of $G$. The method generates widely used exponential families such as normal, gamma, Bernoulli, categorical, Wishart, von Mises, Fisher-Bingham and hyperboloid distributions.
Improving Zero-Shot Translation of Low-Resource Languages
Authors: Surafel M. Lakew, Quintino F. Lotito, Matteo Negri, Marco Turchi, Marcello Federico
Date: 2018-11-04 00:00:00
Proceeding: IWSLT 2017 12
Tasks: Machine Translation, Translation

Recent work on multilingual neural machine translation reported competitive
performance with respect to bilingual models and surprisingly good performance
even on (zeroshot) translation directions not observed at training time. We
investigate here a zero-shot translation in a particularly lowresource
multilingual setting. We propose a simple iterative training procedure that
leverages a duality of translations directly generated by the system for the
zero-shot directions. The translations produced by the system (sub-optimal
since they contain mixed language from the shared vocabulary), are then used
together with the original parallel data to feed and iteratively re-train the
multilingual network. Over time, this allows the system to learn from its own
generated and increasingly better output. Our approach shows to be effective in
improving the two zero-shot directions of our multilingual model. In
particular, we observed gains of about 9 BLEU points over a baseline
multilingual model and up to 2.08 BLEU over a pivoting mechanism using two
bilingual models. Further analysis shows that there is also a slight
improvement in the non-zero-shot language directions.
Neural CRF transducers for sequence labeling
Authors: Kai Hu, Zhijian Ou, Min Hu, Junlan Feng
Date: 2018-11-04 00:00:00
Tasks: Chunking, NER, POS, POS Tagging

Conditional random fields (CRFs) have been shown to be one of the most
successful approaches to sequence labeling. Various linear-chain neural CRFs
(NCRFs) are developed to implement the non-linear node potentials in CRFs, but
still keeping the linear-chain hidden structure. In this paper, we propose NCRF
transducers, which consists of two RNNs, one extracting features from
observations and the other capturing (theoretically infinite) long-range
dependencies between labels. Different sequence labeling methods are evaluated
over POS tagging, chunking and NER (English, Dutch). Experiment results show
that NCRF transducers achieve consistent improvements over linear-chain NCRFs
and RNN transducers across all the four tasks, and can improve state-of-the-art
results.
Investigating context features hidden in End-to-End TTS
Authors: Kohki Mametani, Tsuneo Kato, Seiichi Yamamoto
Date: 2018-11-04 00:00:00
Tasks: Feature Engineering, Speech Synthesis

Recent studies have introduced end-to-end TTS, which integrates the
production of context and acoustic features in statistical parametric speech
synthesis. As a result, a single neural network replaced laborious feature
engineering with automated feature learning. However, little is known about
what types of context information end-to-end TTS extracts from text input
before synthesizing speech, and the previous knowledge about context features
is barely utilized. In this work, we first point out the model similarity
between end-to-end TTS and parametric TTS. Based on the similarity, we evaluate
the quality of encoder outputs from an end-to-end TTS system against eight
criteria that are derived from a standard set of context information used in
parametric TTS. We conduct experiments using an evaluation procedure that has
been newly developed in the machine learning literature for quantitative
analysis of neural representations, while adapting it to the TTS domain.
Experimental results show that the encoder outputs reflect both linguistic and
phonetic contexts, such as vowel reduction at phoneme level, lexical stress at
syllable level, and part-of-speech at word level, possibly due to the joint
optimization of context and acoustic features.
Semi-Supervised Confidence Network aided Gated Attention based Recurrent Neural Network for Clickbait Detection
Authors: Amrith Rajagopal Setlur
Date: 2018-11-04 00:00:00
Tasks: Clickbait Detection, General Classification

Clickbaits are catchy headlines that are frequently used by social media
outlets in order to allure its viewers into clicking them and thus leading them
to dubious content. Such venal schemes thrive on exploiting the curiosity of
naive social media users, directing traffic to web pages that won't be visited
otherwise. In this paper, we propose a novel, semi-supervised classification
based approach, that employs attentions sampled from a Gumbel-Softmax
distribution to distill contexts that are fairly important in clickbait
detection. An additional loss over the attention weights is used to encode
prior knowledge. Furthermore, we propose a confidence network that enables
learning over weak labels and improves robustness to noisy labels. We show that
with merely 30% of strongly labeled samples we can achieve over 97% of the
accuracy, of current state of the art methods in clickbait detection.
Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset
Authors: Dana Berman, Deborah Levy, Shai Avidan, Tali treibitz
Date: 2018-11-04 00:00:00
Tasks: Image Dehazing, Image Enhancement, Single Image Dehazing

Underwater images suffer from color distortion and low contrast, because
light is attenuated while it propagates through water. Attenuation under water
varies with wavelength, unlike terrestrial images where attenuation is assumed
to be spectrally uniform. The attenuation depends both on the water body and
the 3D structure of the scene, making color restoration difficult.
  Unlike existing single underwater image enhancement techniques, our method
takes into account multiple spectral profiles of different water types. By
estimating just two additional global parameters: the attenuation ratios of the
blue-red and blue-green color channels, the problem is reduced to single image
dehazing, where all color channels have the same attenuation coefficients.
Since the water type is unknown, we evaluate different parameters out of an
existing library of water types. Each type leads to a different restored image
and the best result is automatically chosen based on color distribution.
  We collected a dataset of images taken in different locations with varying
water properties, showing color charts in the scenes. Moreover, to obtain
ground truth, the 3D structure of the scene was calculated based on stereo
imaging. This dataset enables a quantitative evaluation of restoration
algorithms on natural images and shows the advantage of our method.
A Novel Predictive-Coding-Inspired Variational RNN Model for Online Prediction and Recognition
Authors: Ahmadreza Ahmadi, Jun Tani
Date: 2018-11-04 00:00:00
Tasks: regression

This study introduces PV-RNN, a novel variational RNN inspired by the predictive-coding ideas. The model learns to extract the probabilistic structures hidden in fluctuating temporal patterns by dynamically changing the stochasticity of its latent states. Its architecture attempts to address two major concerns of variational Bayes RNNs: how can latent variables learn meaningful representations and how can the inference model transfer future observations to the latent variables. PV-RNN does both by introducing adaptive vectors mirroring the training data, whose values can then be adapted differently during evaluation. Moreover, prediction errors during backpropagation, rather than external inputs during the forward computation, are used to convey information to the network about the external data. For testing, we introduce error regression for predicting unseen sequences as inspired by predictive coding that leverages those mechanisms. The model introduces a weighting parameter, the meta-prior, to balance the optimization pressure placed on two terms of a lower bound on the marginal likelihood of the sequential data. We test the model on two datasets with probabilistic structures and show that with high values of the meta-prior the network develops deterministic chaos through which the data's randomness is imitated. For low values, the model behaves as a random process. The network performs best on intermediate values, and is able to capture the latent probabilistic structure with good generalization. Analyzing the meta-prior's impact on the network allows to precisely study the theoretical value and practical benefits of incorporating stochastic dynamics in our model. We demonstrate better prediction performance on a robot imitation task with our model using error regression compared to a standard variational Bayes model lacking such a procedure.
Deep Robust Framework for Protein Function Prediction using Variable-Length Protein Sequences
Authors: Ashish Ranjan, Md. Shah Fahad, David Fernandez-Baca, Akshay Deepak, Sudhakar Tripathi
Date: 2018-11-04 00:00:00
Tasks: Protein Function Prediction

Amino acid sequence portrays most intrinsic form of a protein and expresses primary structure of protein. The order of amino acids in a sequence enables a protein to acquire a particular stable conformation that is responsible for the functions of the protein. This relationship between a sequence and its function motivates the need to analyse the sequences for predicting protein functions. Early generation computational methods using BLAST, FASTA, etc. perform function transfer based on sequence similarity with existing databases and are computationally slow. Although machine learning based approaches are fast, they fail to perform well for long protein sequences (i.e., protein sequences with more than 300 amino acid residues). In this paper, we introduce a novel method for construction of two separate feature sets for protein sequences based on analysis of 1) single fixed-sized segments and 2) multi-sized segments, using bi-directional long short-term memory network. Further, model based on proposed feature set is combined with the state of the art Multi-lable Linear Discriminant Analysis (MLDA) features based model to improve the accuracy. Extensive evaluations using separate datasets for biological processes and molecular functions demonstrate promising results for both single-sized and multi-sized segments based feature sets. While former showed an improvement of +3.37% and +5.48%, the latter produces an improvement of +5.38% and +8.00% respectively for two datasets over the state of the art MLDA based classifier. After combining two models, there is a significant improvement of +7.41% and +9.21% respectively for two datasets compared to MLDA based classifier. Specifically, the proposed approach performed well for the long protein sequences and superior overall performance.
A Function Fitting Method
Authors: Rajesh Dachiraju
Date: 2018-11-04 00:00:00
Tasks: Binary Classification

In this article, we describe a function fitting method that has potential applications in machine learning and also prove relevant theorems. The described function fitting method is a convex minimization problem and can be solved using a gradient descent algorithm. We also provide qualitative analysis on fitness to data of this function fitting method. The function fitting problem is also shown to be a solution of a linear, weak partial differential equation(PDE). We describe a way to fit a Sobolev function by giving a method to choose the optimal $\lambda$ parameter. We describe a closed-form solution to the derived PDE, which enables the parametrization of the solution function. We describe a simple numerical solution using a gradient descent algorithm, that converges uniformly to the actual solution. As the functional of the minimization problem is a quadratic form, there also exists a numerical method using linear algebra. Lastly, we give some numerical examples and also numerically demonstrate its application to a binary classification problem.
Bi-Real Net: Binarizing Deep Network Towards Real-Network Performance
Authors: Zechun Liu, Wenhan Luo, Baoyuan Wu, Xin Yang, Wei Liu, Kwang-Ting Cheng
Date: 2018-11-04 00:00:00
Tasks: Depth Estimation

In this paper, we study 1-bit convolutional neural networks (CNNs), of which both the weights and activations are binary. While efficient, the lacking of representational capability and the training difficulty impede 1-bit CNNs from performing as well as real-valued networks. We propose Bi-Real net with a novel training algorithm to tackle these two challenges. To enhance the representational capability, we propagate the real-valued activations generated by each 1-bit convolution via a parameter-free shortcut. To address the training difficulty, we propose a training algorithm using a tighter approximation to the derivative of the sign function, a magnitude-aware gradient for weight updating, a better initialization method, and a two-step scheme for training a deep network. Experiments on ImageNet show that an 18-layer Bi-Real net with the proposed training algorithm achieves 56.4% top-1 classification accuracy, which is 10% higher than the state-of-the-arts (e.g., XNOR-Net) with greater memory saving and lower computational cost. Bi-Real net is also the first to scale up 1-bit CNNs to an ultra-deep network with 152 layers, and achieves 64.5% top-1 accuracy on ImageNet. A 50-layer Bi-Real net shows comparable performance to a real-valued network on the depth estimation task with only a 0.3% accuracy gap.
Improving GAN with neighbors embedding and gradient matching
Authors: Ngoc-Trung Tran, Tuan-Anh Bui, Ngai-Man Cheung
Date: 2018-11-04 00:00:00

We propose two new techniques for training Generative Adversarial Networks
(GANs). Our objectives are to alleviate mode collapse in GAN and improve the
quality of the generated samples. First, we propose neighbor embedding, a
manifold learning-based regularization to explicitly retain local structures of
latent samples in the generated samples. This prevents generator from producing
nearly identical data samples from different latent samples, and reduces mode
collapse. We propose an inverse t-SNE regularizer to achieve this. Second, we
propose a new technique, gradient matching, to align the distributions of the
generated samples and the real samples. As it is challenging to work with
high-dimensional sample distributions, we propose to align these distributions
through the scalar discriminator scores. We constrain the difference between
the discriminator scores of the real samples and generated ones. We further
constrain the difference between the gradients of these discriminator scores.
We derive these constraints from Taylor approximations of the discriminator
function. We perform experiments to demonstrate that our proposed techniques
are computationally simple and easy to be incorporated in existing systems.
When Gradient matching and Neighbour embedding are applied together, our GN-GAN
achieves outstanding results on 1D/2D synthetic, CIFAR-10 and STL-10 datasets,
e.g. FID score of $30.80$ for the STL-10 dataset. Our code is available at:
https://github.com/tntrung/gan
Elastic CRFs for Open-ontology Slot Filling
Authors: Yinpei Dai, Yichi Zhang, Hong Liu, Zhijian Ou, Yi Huang, Junlan Feng
Date: 2018-11-04 00:00:00
Tasks: slot-filling, Slot Filling

Slot filling is a crucial component in task-oriented dialog systems that is used to parse (user) utterances into semantic concepts called slots. An ontology is defined by the collection of slots and the values that each slot can take. The most widely used practice of treating slot filling as a sequence labeling task suffers from two main drawbacks. First, the ontology is usually pre-defined and fixed and therefore is not able to detect new labels for unseen slots. Second, the one-hot encoding of slot labels ignores the correlations between slots with similar semantics, which makes it difficult to share knowledge learned across different domains. To address these problems, we propose a new model called elastic conditional random field (eCRF), where each slot is represented by the embedding of its natural language description and modeled by a CRF layer. New slot values can be detected by eCRF whenever a language description is available for the slot. In our experiment, we show that eCRFs outperform existing models in both in-domain and cross-domain tasks, especially in predicting unseen slots and values.
RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans
Authors: Qiangguo Jin, Zhaopeng Meng, Changming Sun, Leyi Wei, Ran Su
Date: 2018-11-04 00:00:00
Tasks: Brain Tumor Segmentation, Deep Attention, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation, Tumor Segmentation

Automatic extraction of liver and tumor from CT volumes is a challenging task
due to their heterogeneous and diffusive shapes. Recently, 2D and 3D deep
convolutional neural networks have become popular in medical image segmentation
tasks because of the utilization of large labeled datasets to learn
hierarchical features. However, 3D networks have some drawbacks due to their
high cost on computational resources. In this paper, we propose a 3D hybrid
residual attention-aware segmentation method, named RA-UNet, to precisely
extract the liver volume of interests (VOI) and segment tumors from the liver
VOI. The proposed network has a basic architecture as a 3D U-Net which extracts
contextual information combining low-level feature maps with high-level ones.
Attention modules are stacked so that the attention-aware features change
adaptively as the network goes "very deep" and this is made possible by
residual learning. This is the first work that an attention residual mechanism
is used to process medical volumetric images. We evaluated our framework on the
public MICCAI 2017 Liver Tumor Segmentation dataset and the 3DIRCADb dataset.
The results show that our architecture outperforms other state-of-the-art
methods. We also extend our RA-UNet to brain tumor segmentation on the
BraTS2018 and BraTS2017 datasets, and the results indicate that RA-UNet
achieves good performance on a brain tumor segmentation task as well.
A Batched Scalable Multi-Objective Bayesian Optimization Algorithm
Authors: Xi Lin, Hui-Ling Zhen, Zhenhua Li, Qingfu Zhang, Sam Kwong
Date: 2018-11-04 00:00:00
Tasks: Bayesian Optimization

The surrogate-assisted optimization algorithm is a promising approach for
solving expensive multi-objective optimization problems. However, most existing
surrogate-assisted multi-objective optimization algorithms have three main
drawbacks: 1) cannot scale well for solving problems with high dimensional
decision space, 2) cannot incorporate available gradient information, and 3) do
not support batch optimization. These drawbacks prevent their use for solving
many real-world large scale optimization problems. This paper proposes a
batched scalable multi-objective Bayesian optimization algorithm to tackle
these issues. The proposed algorithm uses the Bayesian neural network as the
scalable surrogate model. Powered with Monte Carlo dropout and Sobolov
training, the model can be easily trained and can incorporate available
gradient information. We also propose a novel batch hypervolume upper
confidence bound acquisition function to support batch optimization.
Experimental results on various benchmark problems and a real-world application
demonstrate the efficiency of the proposed algorithm.